data science problem solving steps

5 Structured Thinking Techniques for Data Scientists

Try 1 of these 5 structured thinking techniques as you wrestle with your next data science project.

Structured thinking is a framework for solving unstructured problems — which covers just about all data science problems. Using a structured approach to solve problems not only only helps solve problems faster but also helps identify the parts of the problem that may need some extra attention. 

Think of structured thinking like the map of a city you’re visiting for the first time.Without a map, you’ll probably find it difficult to reach your destination. Even if you did eventually reach your destination, it’ll probably take you at least double the time.

What Is Structured Thinking?

Here’s where the analogy breaks down: Structured thinking is a framework and not a fixed mindset; you can modify these techniques based on the problem you’re trying to solve.  Let’s look at five structured thinking techniques to use in your next data science project .

• Six Step Problem Solving Model
• Eight Disciplines of Problem Solving
• The Drill Down Technique
• The Cynefin Framework
• The 5 Whys Technique

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1. Six Step Problem Solving Model

This technique is the simplest and easiest to use. As the name suggests, this technique uses six steps to solve a problem, which are:

Have a clear and concise problem definition.

Study the roots of the problem.

Brainstorm possible solutions to the problem.

Examine the possible solution and choose the best one.

Implement the solution effectively.

Evaluate the results.

This model follows the mindset of continuous development and improvement. So, on step six, if your results didn’t turn out the way you wanted, go back to step four and choose another solution (or to step one and try to define the problem differently).

My favorite part about this simple technique is how easy it is to alter based on the specific problem you’re attempting to solve. 

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2. Eight Disciplines of Problem Solving

The eight disciplines of problem solving offers a practical plan to solve a problem using an eight-step process. You can think of this technique as an extended, more-detailed version of the six step problem-solving model.

Each of the eight disciplines in this process should move you a step closer to finding the optimal solution to your problem. So, after you’ve got the prerequisites of your problem, you can follow  disciplines D1-D8.

D1 : Put together your team. Having a team with the skills to solve the project can make moving forward much easier.

D2 : Define the problem. Describe the problem using quantifiable terms: the who, what, where, when, why and how.

D3 : Develop a working plan.

D4 : Determine and identify root causes. Identify the root causes of the problem using cause and effect diagrams to map causes against their effects.

D5 : Choose and verify permanent corrections. Based on the root causes, assess the work plan you developed earlier and edit as needed.

D6 : Implement the corrected action plan.

D7 : Assess your results.

D8 : Congratulate your team. After the end of a project, it’s essential to take a step back and appreciate the work you’ve all done before jumping into a new project.

3. The Drill Down Technique

The drill down technique is more suitable for large, complex problems with multiple collaborators. The whole purpose of using this technique is to break down a problem to its roots to make finding solutions that much easier. To use the drill down technique, you first need to create a table. The first column of the table will contain the outlined definition of the problem, followed by a second column containing the factors causing this problem. Finally, the third column will contain the cause of the second column's contents, and you’ll continue to drill down on each column until you reach the root of the problem.

Once you reach the root causes of the symptoms, you can begin developing solutions for the bigger problem.

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4. The Cynefin Framework

The Cynefin framework, like the rest of the techniques, works by breaking down a problem into its root causes to reach an efficient solution. We consider the Cynefin framework a higher-level approach because it requires you to place your problem into one of five contexts.

• Obvious Contexts. In this context, your options are clear, and the cause-and-effect relationships are apparent and easy to point out.
• Complicated Contexts. In this context, the problem might have several correct solutions. In this case, a clear relationship between cause and effect may exist, but it’s not equally apparent to everyone.
• Complex Contexts. If it’s impossible to find a direct answer to your problem, then you’re looking at a complex context. Complex contexts are problems that have unpredictable answers. The best approach here is to follow a trial and error approach.
• Chaotic Contexts. In this context, there is no apparent relationship between cause and effect and our main goal is to establish a correlation between the causes and effects.
• Disorder. The final context is disorder, the most difficult of the contexts to categorize. The only way to diagnose disorder is to eliminate the other contexts and gather further information.

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5. The 5 Whys Technique

Our final technique is the 5 Whys or, as I like to call it, the curious child approach. I think this is the most well-known and natural approach to problem solving.

This technique follows the simple approach of asking “why” five times — like a child would. First, you start with the main problem and ask why it occurred. Then you keep asking why until you reach the root cause of said problem. (Fair warning, you may need to ask more than five whys to find your answer.)

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5 Steps on How to Approach a New Data Science Problem

Many companies struggle to reorganize their decision making around data and implement a coherent data strategy. The problem certainly isn’t lack of data but inability to transform it into actionable insights. Here's how to do it right.

A QUICK SUMMARY – FOR THE BUSY ONES

TABLE OF CONTENTS

Introduction

Data has become the new gold. 85 percent of companies are trying to be data-driven, according to last year’s survey by NewVantage Partners , and the global data science platform market is expected to reach $128.21 billion by 2022, up from $19.75 billion in 2016.

Clearly, data science is not just another buzzword with limited real-world use cases. Yet, many companies struggle to reorganize their decision making around data and implement a coherent data strategy. The problem certainly isn’t lack of data.

In the past few years alone, 90 percent of all of the world’s data has been created, and our current daily data output has reached 2.5 quintillion bytes, which is such a mind-bogglingly large number that it’s difficult to fully appreciate the break-neck pace at which we generate new data.

The real problem is the inability of companies to transform the data they have at their disposal into actionable insights that can be used to make better business decisions, stop threats, and mitigate risks.

In fact, there’s often too much data available to make a clear decision, which is why it’s crucial for companies to know how to approach a new data science problem and understand what types of questions data science can answer.

What types of questions can data science answer?

“Data science and statistics are not magic. They won’t magically fix all of a company’s problems. However, they are useful tools to help companies make more accurate decisions and automate repetitive work and choices that teams need to make,” writes Seattle Data Guy , a data-driven consulting agency.

The questions that can be answered with the help of data science fall under following categories:

• Identifying themes in large data sets : Which server in my server farm needs maintenance the most?
• Identifying anomalies in large data sets : Is this combination of purchases different from what this customer has ordered in the past?
• Predicting the likelihood of something happening : How likely is this user to click on my video?
• Showing how things are connected to one another : What is the topic of this online article?
• Categorizing individual data points : Is this an image of a cat or a mouse?

Of course, this is by no means a complete list of all questions that data science can answer. Even if it were, data science is evolving at such a rapid pace that it would most likely be completely outdated within a year or two from its publication.

Now that we’ve established the types of questions that can be reasonably expected to be answered with the help of data science, it’s time to lay down the steps most data scientists would take when approaching a new data science problem.

Step 1: Define the problem

First, it’s necessary to accurately define the data problem that is to be solved. The problem should be clear, concise, and measurable . Many companies are too vague when defining data problems, which makes it difficult or even impossible for data scientists to translate them into machine code.

Here are some basic characteristics of a well-defined data problem:

• The solution to the problem is likely to have enough positive impact to justify the effort.
• Enough data is available in a usable format.
• Stakeholders are interested in applying data science to solve the problem.

Step 2: Decide on an approach

There are many data science algorithms that can be applied to data, and they can be roughly grouped into the following families:

• Two-class classification : useful for any question that has just two possible answers.
• Multi-class classification : answers a question that has multiple possible answers.
• Anomaly detection : identifies data points that are not normal.
• Regression : gives a real-valued answer and is useful when looking for a number instead of a class or category.
• Multi-class classification as regression : useful for questions that occur as rankings or comparisons.
• Two-class classification as regression : useful for binary classification problems that can also be reformulated as regression.
• Clustering : answer questions about how data is organized by seeking to separate out a data set into intuitive chunks.
• Dimensionality reduction : reduces the number of random variables under consideration by obtaining a set of principal variables.
• Reinforcement learning algorithms : focus on taking action in an environment so as to maximize some notion of cumulative reward.

Step 3: Collect data

With the problem clearly defined and a suitable approach selected, it’s time to collect data. All collected data should be organized in a log along with collection dates and other helpful metadata.

It’s important to understand that collected data is seldom ready for analysis right away. Most data scientists spend much of their time on data cleaning , which includes removing missing values, identifying duplicate records, and correcting incorrect values.

Step 4: Analyze data

The next step after data collection and cleanup is data analysis. At this stage, there’s a certain chance that the selected data science approach won’t work. This is to be expected and accounted for. Generally, it’s recommended to start with trying all the basic machine learning approaches as they have fewer parameters to alter.

There are many excellent open source data science libraries that can be used to analyze data. Most data science tools are written in Python, Java, or C++.

<blockquote><p>“Tempting as these cool toys are, for most applications the smart initial choice will be to pick a much simpler model, for example using scikit-learn and modeling techniques like simple logistic regression,” – advises Francine Bennett, the CEO and co-founder of Mastodon C.</p></blockquote>

Step 5: Interpret results

After data analysis, it’s finally time to interpret the results. The most important thing to consider is whether the original problem has been solved. You might discover that your model is working but producing subpar results. One way how to deal with this is to add more data and keep retraining the model until satisfied with it.

Most companies today are drowning in data. The global leaders are already using the data they generate to gain competitive advantage, and others are realizing that they must do the same or perish. While transforming an organization to become data-driven is no easy task, the reward is more than worth the effort.

The 5 steps on how to approach a new data science problem we’ve described in this article are meant to illustrate the general problem-solving mindset companies must adopt to successfully face the challenges of our current data-centric era.

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Navigating the Data Science Learning Curve: 6 Essential Tips for Beginners

Ready to dive into data science? Follow these six tips, take action, and unleash your potential in the field

A few weeks ago, I found myself in a conversation that led to an interesting question. It went something like this:

Say, Hans, you’re actually quite active in the field of data science. But when you were studying, data science hadn’t even been invented yet. Still, over the past few years, you’ve gained knowledge and skills in data analytics and data science. So, you must know a thing or two about it. That’s why I have a question for you. What would you recommend if someone wants to learn data science themselves and they have to start from scratch?

Whew, I didn’t have an immediate answer to this question. But it did make me think. What would my advice be? It wasn’t a complex question, but one that triggered many different thoughts and ideas in my mind. How do you go about “learning data science from scratch?” As I pondered, I came up with a list of six tips. And I’m happy to share those tips with you here.

Tip 1: Don’t start with programming, opt for a low-code solution

It might look super cool, typing all sorts of complex Python statements in dark mode, but that’s not what makes me happy. My preference lies with a low-code solution like  KNIME . With the help of KNIME, I’ve been able to accelerate my data science career tremendously. In KNIME, the process is central, not the code. And that comes with several advantages. A KNIME canvas is organized, allowing you to zoom in and out. You can encapsulate nodes into so-called metanodes, and with annotations, the (data science) process you execute in your workflow is supported and made understandable, turning it into a story that you can share and communicate.

Especially if you’re new to data science and don’t have a background or training in programming, it’s important to focus on the problem you want to solve or the insight you want to gain. How do you translate the business problem into a data science problem? That, to me, is the challenge in data science. As a data scientist, you need to be able to focus on the choices you need to make to arrive at a good solution. Which algorithm fits best, which records do I include, which variables should be considered, what metrics do I use to assess the quality of my solution? Those kinds of things. And as a beginner, you don’t want to get stuck every time because you misplaced a comma in your code or forgot a parenthesis, etc.

An additional advantage of KNIME is that during node configuration, all options are presented. Many choices are configurable but also have a default value. This allows you to configure each node thoughtfully or simply see what happens with the default values.

The added value of a data scientist doesn’t lie in writing good code (that’s what an LLM will do for you later) but in conceptualizing, implementing, and making choices to arrive at a process that turns input data into valuable output data. But to have a good plan and make the right choices, you do need some knowledge, but how do you acquire that knowledge?

Tip 2: Get started, just do it

You can read books, watch YouTube videos, browse blogs, take an online course, but if you only consume that content, your skills won’t improve, and your knowledge will only increase to a limited extent. To truly make progress in data science, it’s best to just start and build up knowledge around the data science activities you are doing. Get to work.

Suppose you want to learn how to create a predictive model, and you realize that you need to split your dataset into a training, testing (and validation) set. Dive into this topic and try to figure out the best way to split your dataset for your specific use case. Once you’ve set up partitioning to your satisfaction, move on to the next step in the process. You don’t need to know all the options, but it’s important to understand what you’re doing (and why). Build your workflow or code in small and manageable steps. Try to create a minimal viable product with as few nodes or lines as possible.

It is clear. I go for KNIME as my environment to do my data science and analytics projects. But the choice is based on personal preferences. And this choice is not the key for success to learn data science. Regardless of the approach chosen, the most important factor is consistent practice and hands-on experience in solving real-world data science problems. And yes, in my opinion, KNIME facilitates that the best.

Tip 3: Define a real-world use case with a familiar dataset

Overall, hands-on practice with real-world projects is a fundamental step in the learning journey of data science from scratch. It provides you with practical experience, fosters critical thinking and problem-solving skills, and builds a strong foundation for further exploration and growth in the field of analytics and data science.

If you want to gradually master data science skills, the choice of topic, use case, and datasets is important. It’s better to choose a use case and dataset you’re familiar with than standard datasets (like the Iris dataset) often seen in tutorials. If you don’t have a dataset at hand, check out the  Kaggle Open Datasets .

Working on a topic you’re familiar with and a real dataset associated with it helps evaluate the outcomes of your steps accurately. For instance, if your predictive model for football match outcomes predicts a draw in 80% of the matches, you, as a football expert, know this is incorrect (on average, 25% of matches end in a draw). That means going back to the drawing board. Or if you encounter outliers, such as a team scoring more than 15 goals in a match, you can use your domain knowledge of football to decide if this might be incorrectly entered data or a valid value. Therefore, it’s recommended to work with a real dataset because these datasets confront you with deviations and noise that require attention. On the other hand, the advantage of working with “pre-existing datasets” like the wine dataset, the Iris dataset, or the Boston housing dataset is that they yield consistent results and sometimes seem too good to be true. You can use them effectively to get your workflow “working.” However, you’re not challenged to think about the outcomes.

But problem solving skills are also a part of data science. There for you have to approach problems analytically, question assumptions, and think creatively to find innovative solutions and stimulate critical thinking and decision-making abilities.

Tip 4: Take small, manageable steps

A data science use case like creating a predictive model can be accomplished with a limited number of nodes (see figure).

You probably won’t have the best model right away, but you’ll have a workflow that you can improve by adding functionalities (KNIME nodes) simply and step by step. Pause with each node addition to consider how to best configure it. Do I accept the default settings, or do I investigate the effect of deviating from the standard settings? Expanding the workflow provides opportunities to seek information by reading a blog on the topic, following a YouTube tutorial, or maybe taking a short training session, all specifically focused on the subject you’re currently working on in your workflow and want to learn more about. Reflection allows you to assess your growth, identify areas for improvement, and track your journey in mastering data science.

Tip 5: When stuck, don’t panic

One of the beautiful aspects of working on a data science use case is that it’s not a straight line to the finish. I often feel there’s always room for improvement or doing things differently. This means lots of testing and experimenting to arrive at a good, acceptable solution. However, reaching that good solution often involves overcoming various obstacles. It’s good to know that help is always nearby. If you search smartly on the internet, someone has likely found a solution to the problem you’re facing. And if you get stuck in KNIME, there’s the  KNIME Forum ,  KNIME videos , and the  KNIME Learning Centre .

But perhaps most importantly, don’t give up; keep trying. It won’t always be easy. It’s an illusion to become a full-fledged data scientist in a week. Learning new things happens in steps, and it’s faster when you combine practice with theory. But do it in moderation. It’s better to spend one hour a day for 8 days learning something new than trying to do it all in one day for 8 hours.

Tip 6: Stay motivated and curious, keep on learning

Becoming more proficient in data science doesn’t happen overnight. It takes time. Additionally, data science is more than just programming. It requires knowledge of methods and techniques, as well as the domain in which the data science use case operates.

Seek collaboration and networking within the data science community. Participate in forums, attend meetups, and connect with peers and professionals in the field. Collaborating with others can provide you with valuable insights, feedback, and opportunities for growth.

Your learning journey never ends. Try to stay updated with the latest trends, tools, and technologies in data science. Explore new areas, take advanced courses, and participate in workshops or conferences to expand your knowledge and expertise.

I will never tell you that it is easy to learn data science from scratch. Sometimes it’s easy, sometimes you get stuck. And occasionally your project will fail completely. Therefore, embrace failure as part of the process and let it fuel your motivation to keep learning and growing.

Starting your journey to learn data science from scratch? Here are six tips to guide you along the way.

1. Start with a low-code solution like KNIME to ease into the field without getting bogged down in programming syntax.

2. Dive into hands-on projects, applying your knowledge to real-world datasets and problems.

3. Choose familiar use cases and datasets to better understand the outcomes of your analysis and hone your problem-solving skills.

4. Take small, manageable steps, building on your skills iteratively as you progress.

5. Don’t panic when you encounter obstacles; seek help, stay persistent, and keep trying.

6. Embrace failure, stay motivated, curious, and committed to lifelong learning, continuously expanding your knowledge and staying updated with the latest trends and technologies in data science.

Ready to dive into data science? Follow these six tips, take action, and unleash your potential in the field. Your data science adventure awaits!

This blogpost was inspired by my contribution to a KNIME Webinar “How to teach yourself data science from scratch”.

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Key skills for aspiring data scientists: Problem solving and the scientific method

This blog is part two of our ‘Data science skills’ series, which takes a detailed look at the skills aspiring data scientists need to ace interviews, get exciting projects, and progress in the industry. You can find the other blogs in our series under the ‘Data science career skills’ tag. 

One of the things that attracts a lot of aspiring data scientists to the field is a love of problem solving, more specifically problem solving using the scientific method. This has been around for hundreds of years, but the vast volume of data available today offers new and exciting ways to test all manner of different hypotheses – it is called data science after all. 

If you’re a PhD student, you’ll probably be fairly used to using the scientific method in an academic context, but problem solving means something slightly different in a commercial context. To succeed, you’ll need to learn how to solve problems quickly, effectively and within the constraints of your organisation’s structure, resources and time frames. 

Why is problem solving essential for data scientists? 

Problem solving is involved in nearly every aspect of a typical data science project from start to finish. Indeed, almost all data science projects can be thought of as one long problem solving exercise.

To make this clear, let’s consider the following case study; you have been asked to help optimize a company’s direct marketing, which consists of weekly catalogues. 

Defining the right question 

The first aim of most data science projects is to properly specify the question or problem you wish to tackle. This might sound trivial, but it can often be one of the most challenging parts of any project, and how successful you are at this stage can come to define how successful you are by the finish.

In an academic context, your problem is usually very clearly defined. But as a data scientist in industry it’s rare for your colleagues or your customer to know exactly which problem they’re trying to solve.  

In this example, you have been asked to “optimise a company’s direct marketing”. There are numerous translations of this problem statement into the language of data science. You could create a model which helps you contact customers who would get the biggest uplift in purchase propensity or spend from receiving direct marketing. Or you could simply work out which customers are most likely to buy and focus on contacting them. 

While most marketers and data scientists would agree that the first approach is better in theory, whether or not you can answer this question through data depends on what the company has been doing up to this point. A robust analysis of the company’s data and previous strategy is therefore required, even before deciding on which specific problem to focus on.

This example makes clear the importance of properly defining your question up front; both options here would lead you on very different trajectories and it is therefore crucial that you start off on the right one.  As a data scientist, it will be your job to help turn an often vague direction from a customer or colleague into a firm strategy.

Formulating and evaluating hypotheses

Once you’ve decided on the question that will deliver the best results for your company or your customer, the next step is to formulate hypotheses to test. These can come from many places, whether it be the data, business experts, or your own intuition.

Suppose in this example you’ve had to settle for finding customers who are most likely to buy. Clearly you’ll want to ensure that your new process is better than the company’s old one – indeed, if you’re making better data driven decisions than the company’s previous process you would expect this to be the case.

There is a challenge here though – you can’t directly test the effect of changing historical mailing decisions because these decisions have already been made. However, you can indirectly, by looking at people who were mailed, and then looking at who bought something and who didn’t. If your new process is superior to the previous one, it should be suggesting that you mail most of the people in this first category, as people missed here could indicate potential lost revenue. It should also omit most of the people in the latter category, as mailing this group is definitely wasted marketing spend. 

While these metrics don’t prove that your new process is better, they do provide some evidence that you’re making improvements over what went before.

This example is typical of applied data science projects – you often can’t test your model on historical data to the extent that you would like, so you have to use the data you have available as best you can to give us as much evidence as is possible as to the validity of your hypotheses.

Testing and drawing conclusions

The ultimate test of any data science algorithm is how it performs in the real world. Most data science projects will end by attempting to answer this question, as ultimately this is the only way that data science can truly deliver value to people.

In our example from above, this might look like comparing your algorithm against the company’s current process by doing an randomised control trial (RCT), and comparing the response rates across the two groups. Of course one would expect random variation, and being able to explain the significance (or lack thereof) of any deviations between the two groups would be essential to solving the company’s original problem.

How successfully you test and draw your final conclusions, as well as well you take into account all the limitations with the evaluation, will ultimately decide how impactful the end result of the project is. When addressing a business problem there can be massive consequences to getting the answer wrong – therefore formulating this final test in a way that is scientifically robust but also helps address the original problem statement is therefore paramount, and is a skill that any data scientist needs to possess.

How to develop your problem solving skills

There are certainly ways you can develop your applied data science problem solving skills. The best advice, as so often is true in life, is to practice. Indeed, one of the reasons that so many employers look for data scientists with PhDs is because this demonstrates that the individual in question can solve hard problems. 

Websites like kaggle can be a great starting point for learning how to tackle data science problems and winners of old competitions often have good posts about how they came to build their winning model. It’s also important to learn how to translate business problems into a clear data science problem statement. Data science problems found online have often solved this bit for you, so try and focus on those that are vague and ill-defined – whilst it might be tempting to stick to those that are more concrete, real life is seldom as accommodating.

As the best way to develop your skills is to practice them, Faculty’s Fellowship programme can be a fantastic way to improve your problem solving skills. As the fellowship gives you an opportunity to tackle a real business problem for a real customer, and take the problem through from start to finish, there are not many better ways to develop, and prove, your skills in this area.

Head to the Faculty Fellowship page to find out more. 

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Data Science Process

If you are in a technical domain or a student with a technical background then you must have heard about Data Science from some source certainly. This is one of the booming fields in today’s tech market. And this will keep going on as the upcoming world is becoming more and more digital day by day. And the data certainly hold the capacity to create a new future. In this article, we will learn about Data Science and the process which is included in this.

What is Data Science?

Data can be proved to be very fruitful if we know how to manipulate it to get hidden patterns from them. This logic behind the data or the process behind the manipulation is what is known as Data Science . From formulating the problem statement and collection of data to extracting the required results from them the Data Science process and the professional who ensures that the whole process is going smoothly or not is known as the Data Scientist. But there are other job roles as well in this domain like:

• Data Engineers
• Data Analysts
• Data Architect
• Machine Learning Engineer
• Deep Learning Engineer

Data Science Process Life Cycle

Some steps are necessary for any of the tasks that are being done in the field of data science to derive any fruitful results from the data at hand.

• Data Collection – After formulating any problem statement the main task is to calculate data that can help us in our analysis and manipulation. Sometimes data is collected by performing some kind of survey and there are times when it is done by performing scrapping.
• Data Cleaning – Most of the real-world data is not structured and requires cleaning and conversion into structured data before it can be used for any analysis or modeling.
• Exploratory Data Analysis – This is the step in which we try to find the hidden patterns in the data at hand. Also, we try to analyze different factors which affect the target variable and the extent to which it does so. How the independent features are related to each other and what can be done to achieve the desired results all these answers can be extracted from this process as well. This also gives us a direction in which we should work to get started with the modeling process. 
• Model Building – Different types of machine learning algorithms as well as techniques have been developed which can easily identify complex patterns in the data which will be a very tedious task to be done by a human.
• Model Deployment – After a model is developed and gives better results on the holdout or the real-world dataset then we deploy it and monitor its performance. This is the main part where we use our learning from the data to be applied in real-world applications and use cases.

Components of Data Science Process

Data Science is a very vast field and to get the best out of the data at hand one has to apply multiple methodologies and use different tools to make sure the integrity of the data remains intact throughout the process keeping data privacy in mind. Machine Learning and Data analysis is the part where we focus on the results which can be extracted from the data at hand. But Data engineering is the part in which the main task is to ensure that the data is managed properly and proper data pipelines are created for smooth data flow. If we try to point out the main components of Data Science then it would be:

• Data Analysis –  There are times when there is no need to apply advanced deep learning and complex methods to the data at hand to derive some patterns from it. Due to this before moving on to the modeling part, we first perform an exploratory data analysis to get a basic idea of the data and patterns which are available in it this gives us a direction to work on if we want to apply some complex analysis methods on our data.
• Statistics – It is a natural phenomenon that many real-life datasets follow a normal distribution. And when we already know that a particular dataset follows some known distribution then most of its properties can be analyzed at once. Also, descriptive statistics and correlation and covariances between two features of the dataset help us get a better understanding of how one factor is related to the other in our dataset.
• Data Engineering – When we deal with a large amount of data then we have to make sure that the data is kept safe from any online threats also it is easy to retrieve and make changes in the data as well. To ensure that the data is used efficiently Data Engineers play a crucial role.
• Machine Learning – Machine Learning has opened new horizons which had helped us to build different advanced applications and methodologies so, that the machines become more efficient and provide a personalized experience to each individual and perform tasks in a snap of the hand earlier which requires heavy human labor and time intense.
• Deep Learning – This is also a part of Artificial Intelligence and Machine Learning but it is a bit more advanced than machine learning itself. High computing power and a huge corpus of data have led to the emergence of this field in data science.

Knowledge and Skills for Data Science Professionals

As a Data Scientist, you’ll be responsible for jobs that span three domains of skills.

• Statistical/mathematical reasoning
• Business communication/leadership
• Programming

1. Statistics: Wikipedia defines it as the study of the collection, analysis, interpretation, presentation, and organization of data. Therefore, it shouldn’t be a surprise that data scientists need to know statistics.

2. Programming Language R/ Python: Python and R are one of the most widely used languages by Data Scientists. The primary reason is the number of packages available for Numeric and Scientific computing.

3. Data Extraction, Transformation, and Loading: Suppose we have multiple data sources like MySQL DB, MongoDB, Google Analytics. You have to Extract data from such sources, and then transform it for storing in a proper format or structure for the purposes of querying and analysis. Finally, you have to load the data in the Data Warehouse, where you will analyze the data. So, for people from ETL (Extract Transform and Load) background Data Science can be a good career option.

Steps for Data Science Processes:

Step 1: Defining research goals and creating a project charter

• Spend time understanding the goals and context of your research.Continue asking questions and devising examples until you grasp the exact business expectations, identify how your project fits in the bigger picture, appreciate how your research is going to change the business, and understand how they’ll use your results.

Create a project charter

A project charter requires teamwork, and your input covers at least the following:

• A clear research goal
• The project mission and context
• How you’re going to perform your analysis
• What resources you expect to use
• Proof that it’s an achievable project, or proof of concepts
• Deliverables and a measure of success

Step 2: Retrieving Data

Start with data stored within the company

• Finding data even within your own company can sometimes be a challenge.
• This data can be stored in official data repositories such as databases, data marts, data warehouses, and data lakes maintained by a team of IT professionals.
• Getting access to the data may take time and involve company policies.

Step 3: Cleansing, integrating, and transforming data-

• Data cleansing is a subprocess of the data science process that focuses on removing errors in your data so your data becomes a true and consistent representation of the processes it originates from.
• The first type is the interpretation error, such as incorrect use of terminologies, like saying that a person’s age is greater than 300 years.
• The second type of error points to inconsistencies between data sources or against your company’s standardized values. An example of this class of errors is putting “Female” in one table and “F” in another when they represent the same thing: that the person is female.

Integrating:

• Combining Data from different Data Sources.
• Your data comes from several different places, and in this sub step we focus on integrating these different sources.
• You can perform two operations to combine information from different data sets. The first operation is joining and the second operation is appending or stacking.

Joining Tables:

• Joining tables allows you to combine the information of one observation found in one table with the information that you find in another table.

Appending Tables:

• Appending or stacking tables is effectively adding observations from one table to another table.

Transforming Data

• Certain models require their data to be in a certain shape.

Reducing the Number of Variables

• Sometimes you have too many variables and need to reduce the number because they don’t add new information to the model.
• Having too many variables in your model makes the model difficult to handle, and certain techniques don’t perform well when you overload them with too many input variables.
• Dummy variables can only take two values: true(1) or false(0). They’re used to indicate the absence of a categorical effect that may explain the observation.

Step 4: Exploratory Data Analysis

• During exploratory data analysis you take a deep dive into the data.
• Information becomes much easier to grasp when shown in a picture, therefore you mainly use graphical techniques to gain an understanding of your data and the interactions between variables.
• Bar Plot, Line Plot, Scatter Plot ,Multiple Plots , Pareto Diagram , Link and Brush Diagram ,Histogram , Box and Whisker Plot .

Step 5: Build the Models

• Build the models are the next step, with the goal of making better predictions, classifying objects, or gaining an understanding of the system that are required for modeling.

Step 6: Presenting findings and building applications on top of them –

• The last stage of the data science process is where your soft skills will be most useful, and yes, they’re extremely important.
• Presenting your results to the stakeholders and industrializing your analysis process for repetitive reuse and integration with other tools.

Benefits and uses of data science and big data

• Governmental organizations are also aware of data’s value. A data scientist in a governmental organization gets to work on diverse projects such as detecting fraud and other criminal activity or optimizing project funding.
• Nongovernmental organizations (NGOs) are also no strangers to using data. They use it to raise money and defend their causes. The World Wildlife Fund (WWF), for instance, employs data scientists to increase the effectiveness of their fundraising efforts.
• Universities use data science in their research but also to enhance the study experience of their students. • Ex: MOOC’s- Massive open online courses.

Tools for Data Science Process

As time has passed tools to perform different tasks in Data Science have evolved to a great extent. Different software like Matlab and Power BI , and programming Languages like Python and R Programming Language provides many utility features which help us to complete most of the most complex task within a very limited time and efficiently. Some of the tools which are very popular in this domain of Data Science are shown in the below image.

Usage of Data Science Process

The Data Science Process is a systematic approach to solving data-related problems and consists of the following steps:

• Problem Definition: Clearly defining the problem and identifying the goal of the analysis.
• Data Collection : Gathering and acquiring data from various sources, including data cleaning and preparation.
• Data Exploration: Exploring the data to gain insights and identify trends, patterns, and relationships.
• Data Modeling: Building mathematical models and algorithms to solve problems and make predictions.
• Evaluation: Evaluating the model’s performance and accuracy using appropriate metrics.
• Deployment: Deploying the model in a production environment to make predictions or automate decision-making processes.
• Monitoring and Maintenance: Monitoring the model’s performance over time and making updates as needed to improve accuracy.

Issues of Data Science Process

• Data Quality and Availability : Data quality can affect the accuracy of the models developed and therefore, it is important to ensure that the data is accurate, complete, and consistent. Data availability can also be an issue, as the data required for analysis may not be readily available or accessible.
• Bias in Data and Algorithms : Bias can exist in data due to sampling techniques, measurement errors, or imbalanced datasets, which can affect the accuracy of models. Algorithms can also perpetuate existing societal biases, leading to unfair or discriminatory outcomes.
• Model Overfitting and Underfitting : Overfitting occurs when a model is too complex and fits the training data too well, but fails to generalize to new data. On the other hand, underfitting occurs when a model is too simple and is not able to capture the underlying relationships in the data.
• Model Interpretability : Complex models can be difficult to interpret and understand, making it challenging to explain the model’s decisions and decisions. This can be an issue when it comes to making business decisions or gaining stakeholder buy-in.
• Privacy and Ethical Considerations : Data science often involves the collection and analysis of sensitive personal information, leading to privacy and ethical concerns. It is important to consider privacy implications and ensure that data is used in a responsible and ethical manner.
• Technical Challenges : Technical challenges can arise during the data science process such as data storage and processing, algorithm selection, and computational scalability.

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Steps to Solve a Data Science Problem

• September 5, 2023
• Machine Learning

Everyone has their own way of approaching a Data Science problem. If you are a beginner in Data Science, then your way of approaching the problem will develop over time. But there are some steps you should follow to start and reach the end of your problem with a solution. So, if you want to know the steps you should follow while solving a Data Science problem, this article is for you. In this article, I’ll take you through all the essential steps you should follow to solve a Data Science problem.

Below are all the steps you should follow to solve a Data Science problem:

• Define the Problem
• Data Collection
• Data Cleaning
• Explore the Data
• Feature Engineering
• Choose a Model
• Split the Data
• Model Training and Evaluation

Now, let’s go through each step one by one.

Step 1: Define the Problem

When solving a data science problem, the initial and foundational step is to define the nature and scope of the problem. It involves gaining a comprehensive understanding of the objectives, requirements, and limitations associated. By going through this step in the beginning, data scientists lay the groundwork for a structured and effective analytical process.

When defining the problem, data scientists need to answer several crucial questions. What is the ultimate goal of this analysis? What specific outcomes are expected? Are there any constraints or limitations that need to be considered? It could involve factors like available data, resources, and time constraints.

For instance, imagine a Data Science problem where an e-commerce company aims to optimize its recommendation system to boost sales. The problem definition here would encompass aspects like identifying the target metrics (e.g., click-through rate, conversion rate), understanding the available data (user interactions, purchase history), and recognizing any challenges that might arise (data privacy concerns, computational limitations).

So, the first step of defining the problem sets the stage for the entire steps to solve a Data Science problem. It establishes a roadmap, aids in effective resource allocation, and ensures that the subsequent analytical efforts are purpose-driven and oriented towards achieving the desired outcomes.

Step 2: Data Collection

The second critical step is the collection of relevant data from various sources. This step involves the procurement of raw information that serves as the foundation for subsequent analysis and insights.

The data collection process encompasses a variety of sources, which could range from databases and APIs to files and web scraping . Each source contributes to the diversity and comprehensiveness of the data pool. However, the key lies not just in collecting data but in ensuring its accuracy, completeness, and representativeness.

For instance, imagine a retail company aiming to optimize its inventory management. To achieve this, the company might collect data on sales transactions, stock levels, and customer purchasing behaviour. This data could be collected from internal databases, external vendors, and customer interaction logs.

So, the data collection phase is about assembling a robust and reliable dataset that will be the foundation for subsequent analysis in the rest of the steps to solve a Data Science problem.

Step 3: Data Cleaning

Once relevant data is collected, the next crucial step in solving a data science problem is data cleaning . Data cleaning involves refining the collected data to ensure its quality, consistency, and suitability for analysis.

The cleaning process entails addressing various issues that may be present in the dataset. One common challenge is handling missing values, where certain data points are absent. It can occur due to various reasons, such as data entry errors or incomplete records. To address this, data scientists apply techniques like imputation, where missing values are estimated and filled in based on patterns within the data.

Outliers , which are data points that deviate significantly from the rest of the dataset, can also impact the integrity of the analysis. Outliers could be due to errors or represent genuine anomalies. Data cleaning involves identifying and either removing or appropriately treating these outliers, as they can distort the results of analysis.

Inconsistencies and errors in the data, such as duplicate records or contradictory information, can arise from various sources. These discrepancies need to be detected and rectified to ensure the accuracy of analysis. Data cleaning also involves standardizing units of measurement, ensuring consistent formatting, and addressing other inconsistencies.

Preprocessing is another crucial aspect of data cleaning. It involves transforming and structuring the data into a usable format for analysis. It might include normalization, where data is scaled to a common range, or encoding categorical variables into numerical representations.

So, data cleaning is an essential step in preparing the data for analysis. It ensures that the data is accurate, reliable, and ready to be used for the rest of the steps to solve a Data Science problem. By addressing missing values, outliers, and inconsistencies, data scientists create a solid foundation upon which subsequent analysis can be performed effectively.

Step 4: Explore the Data

After the data has been cleaned and prepared, the next crucial step in solving a data science problem is exploring the data . Exploring the data involves delving into its characteristics, patterns, and relationships to extract meaningful insights that can inform subsequent analyses and decision-making.

Data exploration encompasses techniques that are aimed to uncover hidden patterns and gain a deeper understanding of the dataset. Visualizations and summary statistics are commonly used tools during this step. Visualizations, such as graphs and charts, provide a visual representation of the data, making it easier to identify trends, anomalies, and relationships.

For example, consider a retail dataset containing information about customer purchases. Data exploration could involve creating visualizations of customer spending patterns over different months and identifying if there are any particular items that are frequently purchased together. It can provide insights into customer preferences and inform targeted marketing strategies.

So, data exploration is like peering into the data’s story, uncovering its nuances and intricacies. It helps data scientists gain a comprehensive understanding of the dataset, enabling them to make informed decisions about the analytical techniques to be employed in the next steps to solve a Data Science problem. By identifying trends, anomalies, and relationships, data exploration sets the stage for more sophisticated analyses and ultimately contributes to making impactful business decisions.

Step 5: Feature Engineering

The next step is feature engineering, where the magic of transformation takes place. Feature engineering involves crafting new variables from the existing data that can provide deeper insights or improve the performance of machine learning models.

Feature engineering is like refining raw materials to create a more valuable product. Just as a skilled craftsman shapes and polishes raw materials into a finished masterpiece, data scientists carefully craft new features from the available data to enhance its predictive power. Feature engineering encompasses a variety of techniques. It involves performing statistical and mathematical calculations on the existing variables to derive new insights.

Consider a retail scenario where the goal is to predict customer purchase behaviour. Feature engineering might involve creating a new variable that represents the average purchase value per customer, combining information about the number of purchases and total spent. This aggregated metric can provide a more holistic view of customer spending patterns.

So, feature engineering means transforming data into meaningful features that drive better predictions and insights. It’s the bridge that connects the raw data to the models, enhancing their performance and contributing to the overall success while solving a Data Science problem.

Step 6: Choose a Model

The next step is selecting a model to choose the right tool for the job. It’s the stage where you decide which machine learning algorithm best suits the nature of your problem and aligns with your objectives.

Model selection depends on understanding the fundamental nature of your problem. Is it about classifying items into categories, predicting numerical values, identifying patterns in data, or something else? Different machine learning algorithms are designed to tackle specific types of problems, and choosing the right one can significantly impact the quality of your results.

For instance, if your goal is to predict a numerical value, regression algorithms like linear regression, decision trees, or support vector regression might be suitable. On the other hand, if you’re dealing with classification tasks, where you need to assign items to different categories, algorithms like logistic regression, random forests, decision tree classifier, or support vector machines might be more appropriate.

So, selecting a model is about finding the best tool to unlock the insights hidden within your data. It’s a strategic decision that requires careful consideration of the problem’s nature, the data’s characteristics, and the algorithm’s capabilities.

Step 7: Split the Data

Imagine the process of solving a data science problem as building a bridge of understanding between the past and the future. In this step, known as data splitting, we create a pathway that allows us to learn from the past and predict the future with confidence.

The concept is simple: you wouldn’t drive a car without knowing how it handles different road surfaces. Similarly, you wouldn’t build a predictive model without first understanding how it performs on different sets of data. Data splitting is about creating distinct sets of data, each with a specific purpose, to ensure the reliability and accuracy of your model.

Firstly, we divide our data into three key segments: the training, the validation, and the test set. Think of these as different stages of our journey: the training set serves as the learning ground where our model builds its understanding of patterns and relationships in the data. Next, the validation set helps us fine-tune our model’s settings, known as hyperparameters, to ensure it’s optimized for performance. Lastly, the test set is the true test of our model’s mettle. It’s a simulation of the real-world challenges our model will face.

Why the division? Well, if we used all our data for training, we risk creating a model that’s too familiar with the specifics of our data and unable to generalize to new situations. By having separate validation and test sets, we avoid over-optimization, making our model robust and capable of navigating diverse scenarios.

So, data splitting isn’t just a division of numbers; it’s a strategic move to ensure that our models learn, adapt, and predict effectively. It’s about providing the right environment for learning, tuning, and testing so that our predictive journey leads to reliable and accurate outcomes.

Final Step: Model Training and Evaluation

The final step to solve a Data science problem is Model Training and Evaluation. 

The first aspect of this step is Model Training. With the chosen algorithm, the model is presented with the training data. The model grasps the underlying patterns, relationships, and trends hidden within the data. It adapts its internal parameters to mould itself according to the intricacies of the training examples. Then the model is evaluated on the test set. Metrics like accuracy, precision, recall, and F1-score provide insights into how well the model is performing.

So, in the final step, we train the chosen model on the training data. It involves fitting the model to learn patterns from the data. And evaluate the model’s performance on the test set.

So, below are all the steps you should follow to solve a Data Science problem:

I hope you liked this article on steps to solve a Data Science problem. Feel free to ask valuable questions in the comments section below.

Aman Kharwal

Data Strategist at Statso. My aim is to decode data science for the real world in the most simple words.

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5 Key Steps in the Data Science Lifecycle Explained

Data science has emerged as one of the most popular and valuable fields in the modern landscape of business and management. It is, therefore, important to know the data science lifecycle when applying data to extract information and solve problems.

This lifecycle encompasses five key stages, that include—data collection, data pre-processing, data processing, data mining, and data presentation and dissemination. All of these are crucial in the process of converting raw data into valuable knowledge to support projects and their execution.

Data Collection: The Foundation of the Data Science Lifecycle

Data collection is the first and perhaps the most important process of the data science life cycle. This phase involves the identification of data from various sources, which is the basis for the analysis and modeling. The quality of the data set gathered in this particular stage of the process has a direct relationship with the quality of the insights that will be generated in the subsequent stages of the process.

The first step focuses on the sources of data, which may be insider databases, outsider APIs, questionnaires, sensors, or web crawling. All sources present different kinds of information, must be chosen for the project. The objective is to get data that is valid, reliable, and useful in solving the problem being considered.

Key considerations in data collection:

• Source Identification and Selection: Select data sources that are relevant to the project, considering the project’s objectives. Thus, internal sources, for example, CRM, can reveal more specific information, while external sources, like social networks or market research, can give a broader and more general view.
• Data Quality and Integrity: Evaluate the effectiveness of each source of data for research questions. Check to make sure that everything is correct, and that all data is current. Ensure that there is a mechanism for checking and correcting any errors or inconsistencies before proceeding to the next steps.
• Ethical and Legal Compliance: It is essential to follow ethical standards and legal considerations while collecting the data. This involves getting legal clearance, observing the privacy policies, and implementing measures to prevent data leakages.

Incorporating these considerations into the data collection process helps to build a strong foundation for the other stages of the data science lifecycle and, hence, leads to better results.

Data Preparation: Transforming Raw Data into Actionable Insights

Data cleaning is one of the most critical steps in the data science process, as it paves the way for data exploration and modeling. This step entails the process of preparing the data for analysis by arranging it in a way that is easy to analyze. Data preparation is crucial to ensure that the conclusions made are correct and can be relied on when coming up with business decisions.

Data preparation encompasses several key activities:

• Data Cleaning: This is the process of checking and correcting any errors or inconsistencies in the data. Some of the most frequent operations include filling in the missing data, eliminating the records’ duplicates, and dealing with outliers. Techniques like Pandas and SQL are used for such purposes most of the time.
• Data Transformation: The data is usually presented in different formats and must be reformatted for analysis purposes. This may include handling numerical data by scaling the numbers, handling categorical data through coding, and combining data from different datasets. Tools like feature scaling and one-hot encoding are very useful in this process.
• Data Integration: It is very important to integrate different sources of information into a single database. The process may include data integration , data synchronization, and data cleaning, where data from different sources is combined, structured, and standardized.
• Data Reduction: For higher efficiency and easy control, large data may need to be down-sampled. Data reduction methods, such as dimensionality reduction and sampling, can help reduce the size of the data while preserving important information.

Data cleaning is crucial, as it is the initial phase of the data science process, ensuring efficient and accurate analysis and model development.

In-depth Analysis of the Data Science Lifecycle

Data Analysis is the third step in the Data Science process, where raw data is analyzed to obtain useful information. This process has several methods for the identification of patterns in data in a manner that can inform strategy.

Key Aspects of Data Analysis:

• Descriptive Analysis: Gives historical information in the form of averages, median, and dispersion with the help of standard deviation. It assists in ascertaining the previous results and patterns.
• Diagnostic Analysis: Emphasizes analyzing the causes of previous results. Correlation analysis and hypothesis testing are used to understand why some events happened in a particular manner.
• Predictive Analysis: It employs statistical models and machine learning algorithms to predict the possible occurrence of future events from past events. Some of them are regression analysis and time series forecasting.
• Prescriptive Analysis: Proposes a course of action because of the descriptive, diagnostic, and predictive analysis done on the insights. It usually encompasses an optimization procedure that helps in determining the most appropriate decision.

Data analysis tools and technologies that are useful for the task include Python libraries (Pandas, NumPy), R, and data visualization tools (Tableau, Power BI). It is vital to understand these techniques and tools for extracting value from large and often unstructured datasets.

Data Modeling: Crafting the Predictive Blueprint

Data modeling is an important step in the data science process, where the raw data is transformed into valuable information using various analytical and predictive methods. The first phase is to translate the collected data into mathematical and statistical models and analyze the data to discover patterns and relationships.

Key Aspects of Data Modeling :

• Model Selection: Selecting the right model based on the nature of the problem; for instance, using the regression model for continuous data and classification model for categorical data.
• Model Training: Utilizing past data to train the model, thus enabling it to identify the patterns and correlations within the data.
• Model Evaluation: Measuring and comparing metrics, such as accuracy, precision, recall, and F1 score, for the model to check how well it is functioning.
• Model tuning: Optimizing the parameters of the model when fitting it to the data to avoid overfitting or underfitting.

Other approaches, such as ensemble models and deep learning , can enhance data modeling by providing better analysis. This can be done using Python libraries such as sci-kit-learn and TensorFlow or platforms such as Azure ML in the data science process.

Effective Data Visualization and Communication

Effective data visualization and communication is used to present the data in a way that is understandable and can be converted into useful information. At this stage, data are presented visually to detect patterns, trends, and relationships that may not be obvious from the numerical data.

Effective visualization enables stakeholders to understand complex ideas within a short time and come up with the right decisions. Key techniques include:

• Choosing the Right Visuals: Identifying the right charts, graphs, and maps that are most suited to present the data in the given case, like using bar graphs for comparing or line graphs for presenting trends,
• Clarity and Simplicity: Simple and clear visualization, avoiding overcrowding of the visual, and proper labels and legends.
• Storytelling: Transforming the information into a story to make the findings and recommendations easily understandable to the audience.

Tools and technologies play a significant role in this process, including:

• Tableau: To design engaging and easily sharable dashboards.
• Power BI : For data connectivity with different data sources and generating comprehensive reports.
• Matplotlib and Seaborn: For generating static, interactive, and animated plots in Python.

These techniques guarantee that the findings from the data are well presented and understood by the stakeholders, who can then take proper action.

It is critical to grasp the concept of the data science lifecycle when dealing with the challenges of data science . The five steps—data collection, data preparation, data analysis, data modeling, and data visualization and communication—are crucial in the process of deriving insight from data. Following these stages provides a structured approach to problem-solving and value creation, highlighting the importance of each stage in achieving positive results.

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Step by Step process to solve a Data Science Challenge/Problem

December 29, 2019 6 min read

From predicting a sales forecast to predicting the shortest route to reach a destination, Data Science has a wide range of applications across various industries. Engineering, marketing, sales, operations, supply chain, and whatnot. You name it, and there is an application of data science. And the application of data science is growing exponentially! The situation is such that the demand for people with knowledge in data science is higher than academia is currently supplying!  

Starting with this article, I will be writing a series of blog posts on how to solve a Data Science problem in real-life and in data science competitions.

While there could be different approaches to solving a problem, the broad structure to solving a Data Science problem remains more or less the same. The approach that I usually follow is mentioned below.

Step 1: Identify the problem and know it well: 

In real-life scenarios: Identification of a problem and understanding the problem statement is one of the most critical steps in the entire process of solving a problem. One needs to do high-level analysis on the data and talk to relevant functions (could be marketing, operations, technology team, product team, etc) in the organization to understand the problems and see how these problems can be validated and solved through data.

To give a real-life example, I will briefly take you through a problem that I worked on recently. I was performing the customer retention analysis of an e-learning platform. This particular case is a classification problem where the target variable is binary i.e one needs to predict whether a user would be an active learner on the platform or not in the next ‘x’ days, based on her behavior/interaction on the platform for the last ‘y’ days. 

As I just mentioned, identification of the problem is one of the most critical steps. In this particular case, it was identifying that there is an issue with the user retention on the platform. And as an immediate actionable step, it is important to understand the underlying factors that are causing users to leave the platform (or become non-active learners). Now the question is how do we do this. 

In the case of Data Science challenges , the problem statement is generally well defined, and all you need to do is clearly understand it and come up with a suitable solution. If needed, an additional primary and secondary research about the problem statement should be done as it helps in coming up with a better solution. If needed additional variables and cross features can be created based on the subject expertise.

Step 2: Get the relevant data:

In real-life scenarios: Once the problem is identified, Data scientists need to talk to relevant functions (could be marketing, operations, technology team, product team, etc) in the organization to understand the possible trigger points of the problem and identify relevant data to perform the analysis. Once this is done, all the relevant data should be extracted from the database. 

Continuing my narration of the problem statement I recently worked on, I did a thorough audit of the platform and the user journey and what actions users performed when on the platform. I did the audit with the help of the product and development team. This audit gave me a thorough understanding of the database architecture and potential data logs that were captured and could be considered for the analysis. An extensive list of data points (variables or features) were collated with the help of relevant stakeholders in the organization. 

In essence, usually, this step not only helps in understanding the DB architecture and data extraction process, but it would also help in identifying potential issues within the DB (if any), missing logs in the user journey that were not captured previously, etc. This would further help the development team to add the missing logs and enhance the architecture of the DB. 

Now that we have done the data extraction, we can proceed with the data pre-processing step in order to prepare the data for the analysis.

Data Science Challenge: In the case of Data Science Challenges, a dataset is often provided.

Step 3: Perform exploratory data analysis:

To begin with, data exploration is done to understand the patterns of each of the variables. Some basic plots such as histograms and box plots are analyzed to check if there are any outliers, class imbalances, missingness, and anomalies in the dataset. Data exploration and data pre-processing have a very close correlation and often they are clubbed together.

Step 4: Pre-process the data:

In order to get reliable, reproducible and unbiased data analysis certain pre-processing steps are to be followed. In my recent study, I followed the below-mentioned steps – these are some of the standard steps that are followed while performing any analysis:

• Data Cleaning and treating missingness in the data: Often data comes with missing values and it is always a struggle to get quality data.
• Standardization/normalization (if needed): Often variables in a dataset come with a wide range of data, performing standardization/normalization would bring them to a common scale so that it could further help in implementing various machine learning models (where standardization/normalization is a pre-requisite to apply such models).
• Outlier detection : It is important to know if there are any anomalies in the dataset and treat them if required. Else you might end up getting skewed results.
• Train dataset: Models are trained on the training dataset
• Test dataset: Once the model is built on the training dataset, it should be tested on the test data to check for its performance.

The pre-processing step is common for both real-life data science problems and competitions alike. Now that we have pre-processed the data, we can move to defining the model evaluation parameters and exploring the data further.

Step 5: Define model evaluation parameters :

Arriving at the right parameters to assess a model is critical before performing the analysis.

Based on various parameters and expressions of interest of the problem, one needs to define model evaluation parameters. Some of the widely used model evaluation performance are listed below:

• Receiver Operating Characteristic (ROC): This is a visualization tool that plots the relationship between true positive rate and false positive rate of a binary classifier.  ROC curves can be used to compare the performance of different models by measuring the area under the curve (AUC) of its plotted scores, which ranges from 0.0 to 1.0. The greater this area, the better the algorithm is to find a specific feature.
• Classification Accuracy/Accuracy
• Confusion matrix
• Mean Absolute Error
• Mean Squared Error
• Precision, Recall

The model performance evaluation should be done on the test dataset created during the preprocessing step, this test dataset should remain untouched during the entire model training process.

Coming to the customer retention analysis that I worked on, my goal was to predict the users who would leave the platform or become non-active learners. In this specific case, I picked a model that has a good true positive rate in its confusion matrix. Here, true positive means, the cases in which the model has predicted a positive result (i.e user left the platform or user became a non-active learner) that is the same as the actual output. Let’s not worry about the process of picking the right model evaluation parameter, I will give a detailed explanation in the next series of articles.

Data Science challenges: Often, the model evaluation parameters are given in the challenge.

Step 7: Perform feature engineering:

This step is performed in order to know:

• Important features that are to be used in the model (basically we need to remove the redundant features if any). Metrics such as AIC, BIC are used to identify the redundant features, there are built-in packages such as StepAIC (forward and backward feature selection) in R that help in performing these steps. Also, algorithms such as Boruta are usually helpful in understanding the feature importance.

In my case, I used Boruta to identify the important features that are required for applying a machine learning model. In general, featuring has following steps:

• Transform features: Often a feature or a variable in the dataset might not have a linear relationship with the target variable. We would get to know this in the exploratory data analysis. I usually try and apply various transformations such as inverse, log, polynomial, logit, probit etc that closely matches the relationship between the target variable and the feature
• Create cross features or new relevant variables- We can create cross features based on domain knowledge. For example, if we were given batsmen profile (say Sachin or Virat) with the following data points: name, no. of matches played, total runs scored — we can create a new cross-feature called batting average = runs scored/matches played. 

Once we run the algorithms such as Boruta, we would get feature importance. Now that we know what all features are important, we can proceed to model building exercise.

Step 8: Build the model:

Various machine learning models can be tried based on the problem statement. We can start fitting various models, some of the examples include linear regression, logistic regression, random forest, neural networks etc and enhance the fitted models further (through cross-validation, tuning the hyper-parameters etc). 

Step 9: Perform model comparison:

Now that we have built various models, it is extremely important for us to compare them and identify the best one based on defined problem and model evaluation parameters (defined in step 5).

In my example, I experimented with logistic regression, random forest, decision tree, neural networks, and extreme gradient boosting. Out of all, extreme gradient boosting turned out to be the best model for the given data and problem at hand.

Step 10: Communicate the result:

Data visualization and proper interpretation of the models should be done in this step. This would provide valuable data insights that would immensely help various teams in an organization to make informed data-driven decisions. The final data visualization and communication should be very intuitive such that anyone can understand and interpret the results. Further, the end-user who consumes the data should be able to turn them into actionable points that could further enhance the growth of the organization. Well, this summarizes the steps for solving a data science problem.

Happy learning!

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7 Common Data Science Challenges of 2024 [with Solution]

Home Blog Data Science 7 Common Data Science Challenges of 2024 [with Solution]

Data is the new oil for companies. Since then, it has been a standard aspect of every choice made. Increasingly, businesses rely on analytics and data to strengthen their brand's position in the market and boost revenue.

Information now has more value than physical metals. According to Informatica, 78% of data leaders predicted increased data investment in 2024, and projected to grow from USD 133.12 billion in 2024 to USD 776.86 billion by 2032, exhibiting a CAGR of 24.7% (Fortune Business Insights).

Data science is not a meaningless term with no practical applications. Yet, many businesses have difficulty reorganizing their decision-making around data and implementing a consistent data strategy. Lack of information is not the issue. 

Our daily data production has reached 2.5 quintillion bytes, which is so huge that it is impossible to completely understand the breakneck speed at which we produce new data. Ninety percent of all global data was generated in the previous few years. 

The actual issue is that businesses aren't able to properly use the data they already collect to get useful insights that can be utilized to improve decision-making, counteract risks, and protect against threats. 

It is vital for businesses to know how to approach a new data science challenges and understand what kinds of questions data science can answer since there is frequently too much data accessible to make a clear choice. One must have a look at Data Science Course Subjects  for an outstanding career in Data Science. 

What is Data Science Challenges?

Data science is an application of the scientific method that utilizes data and analytics to address issues that are often difficult (or multiple) and unstructured. The phrase "fishing expedition" comes from the field of analytics and refers to a project that was never structured appropriately, to begin with, and entails searching through the data for unanticipated connections. This particular kind of "data fishing" does not adhere to the principles of efficient data science; nonetheless, it is still rather common. Therefore, the first thing that needs to be done is to clearly define the issue. In the past, we put out an idea for 

"The study of statistics and data is not a kind of witchcraft. They will not, by any means, solve all of the issues that plague a corporation. According to Seattle Data Guy, a data-driven consulting service, "but, they are valuable tools that assist organizations make more accurate judgments and automate repetitious labor and choices that teams need to make." 

The following are some of the categories that may be used to classify the problems that can be solved with the assistance of data science:

• Finding patterns in massive data sets : Which of the servers in my server farm need the most maintenance? 
• Detecting deviations from the norm in huge data sets : Is this particular mix of acquisitions distinct from what this particular consumer has previously ordered? 
• The process of estimating the possibility of something occurring : What are the chances that this person will click on my video? 
• illustrating the ways in which things are related to one another : What exactly is the focus of this article that I saw online? 
• Categorizing specific data points: Which animal do you think this picture depicts a kitty or a mouse? 

Of course, the aforementioned is in no way a comprehensive list of all the questions that can be answered by data science. Even if it were, the field of data science is advancing at such a breakneck speed that it is quite possible that it would be rendered entirely irrelevant within a year or two of its release. 

It is time to write out the stages that the majority of data scientists would follow when tackling a new data science challenge now that we have determined the categories of questions that may be fairly anticipated to be solved with the assistance of data science. Data Science Bootcamp review is for people struggling to make a breakthrough in this domain.

Common Data Science Challenges Faced by Data Scientists

1. preparation of data for smart enterprise ai.

Finding and cleaning up the proper data is a data scientist's priority. Nearly 80% of a data scientist's day is spent on cleaning, organizing, mining, and gathering data, according to a CrowdFlower poll. In this stage, the data is double-checked before undergoing additional analysis and processing. Most data scientists (76%) agree that this is one of the most tedious elements of their work. As part of the data wrangling process, data scientists must efficiently sort through terabytes of data stored in a wide variety of formats and codes on a wide variety of platforms, all while keeping track of changes to such data to avoid data duplication. 

Adopting AI-based tools that help data scientists maintain their edge and increase their efficacy is the best method to deal with this issue. Another flexible workplace AI technology that aids in data preparation and sheds light on the topic at hand is augmented learning. 

2. Generation of Data from Multiple Sources

Data is obtained by organizations in a broad variety of forms from the many programs, software, and tools that they use. Managing voluminous amounts of data is a significant obstacle for data scientists. This method calls for the manual entering of data and compilation, both of which are time-consuming and have the potential to result in unnecessary repeats or erroneous choices. The data may be most valuable when exploited effectively for maximum usefulness in company artificial intelligence. 

Companies now can build up sophisticated virtual data warehouses that are equipped with a centralized platform to combine all of their data sources in a single location. It is possible to modify or manipulate the data that is stored in the central repository to satisfy the needs of a company and increase its efficiency. This easy-to-implement modification has the potential to significantly reduce the amount of time and labor required by data scientists. 

3. Identification of Business Issues

Identifying issues is a crucial component of conducting a solid organization. Before constructing data sets and analyzing data, data scientists should concentrate on identifying enterprise-critical data science challenges. Before establishing the data collection, it is crucial to determine the source of the problem rather than immediately resorting to a mechanical solution. 

Before commencing analytical operations, data scientists may have a structured workflow in place. The process must consider all company stakeholders and important parties. Using specialized dashboard software that provides an assortment of visualization widgets, the enterprise's data may be rendered more understandable. 

4. Communication of Results to Non-Technical Stakeholders

The primary objective of a data scientist is to enhance the organization's capacity for decision-making, which is aligned with the business plan that its function supports. The most difficult obstacle for data scientists to overcome is effectively communicating their findings and interpretations to business leaders and managers. Because the majority of managers or stakeholders are unfamiliar with the tools and technologies used by data scientists, it is vital to provide them with the proper foundation concept to apply the model using business AI. 

In order to provide an effective narrative for their analysis and visualizations of the notion, data scientists need to incorporate concepts such as "data storytelling." 

5. Data Security

Due to the need to scale quickly, businesses have turned to cloud management for the safekeeping of their sensitive information. Cyberattacks and online spoofing have made sensitive data stored in the cloud exposed to the outside world. Strict measures have been enacted to protect data in the central repository against hackers. Data scientists now face additional challenges in data science as they attempt to work around the new restrictions brought forth by the new rules. 

Organizations must use cutting-edge encryption methods and machine learning security solutions to counteract the security threat. In order to maximize productivity, it is essential that the systems be compliant with all applicable safety regulations and designed to deter lengthy audits. 

6. Efficient Collaboration

It is common practice for data scientists and data engineers to collaborate on the same projects for a company. Maintaining strong lines of communication is very necessary to avoid any potential conflicts. To guarantee that the workflows of both teams are comparable, the institution hosting the event should make the necessary efforts to establish clear communication channels. The organization may also choose to establish a Chief Officer position to monitor whether or not both departments are functioning along the same lines. 

7. Selection of Non-Specific KPI Metrics

It is a common misunderstanding that data scientists can handle the majority of the job on their own and come prepared with answers to all of the data science challenges that are encountered by the company. Data scientists are put under a great deal of strain as a result of this, which results in decreased productivity. 

It is vital for any company to have a certain set of metrics to measure the analyses that a data scientist presents. In addition, they have the responsibility of analyzing the effects that these indicators have on the operation of the company. 

The many responsibilities and duties of a data scientist make for a demanding work environment. Nevertheless, it is one of the occupations that are in most demand in the market today. The data science challenges that are experienced by data scientists are simply solvable difficulties that may be used to increase the functionality and efficiency of workplace AI in high-pressure work situations.

Types of Data Science Challenges/Problems

1. data science business challenges.

Listening to important words and phrases is one of the responsibilities of a data scientist during an interview with a line-of-business expert who is discussing a business issue. The data scientist breaks the issue down into a procedural flow that always involves a grasp of the business challenges of data scientist, a comprehension of the data that is necessary, as well as the many forms of artificial intelligence (AI) and data science approaches that can address the problem. This information, when taken as a whole, serves as the impetus behind an iterative series of thought experiments, modeling methodologies, and assessment of the business objectives. 

The company itself has to remain the primary focus. When technology is used too early in a process, it may lead to the solution focusing on the technology itself, while the original business challenge may be ignored or only partially addressed. 

Artificial intelligence and data science demand a degree of accuracy that must be captured from the beginning: 

• Describe the issue that needs to be addressed. 
• Provide as much detail as you can on each of the business questions. 
• Determine any additional business needs, such as maintaining existing client relationships while expanding potential for upselling and cross-selling. 
• Specify the predicted advantages in terms of how they will affect the company, such as a 10% reduction in the customer turnover rate among high-value clients. 

2. Real Life Data Science Problems

Data science is the use of hybrid mathematical and computer science models to address real-world business challenges of data science in order to get actionable insights. It is willing to take the risk of venturing into the unknown domain of 'unstructured' data in order to get significant insights that assist organizations in improving their decision-making. 

• Managing the placement of digital advertisements using computerized processes. 
• The search function will be improved by the use of data science and sophisticated analytics. 
• Using data science for producing data-driven crime predictions 
• Utilizing data science in order to avoid breaking tax laws 

3. Data Science Challenges In Healthcare And Example

It has been calculated that each human being creates around 2 gigabytes of data per day. These measurements include brain activity, tension, heart rate, blood sugar, and many more. These days, we have more sophisticated tools, and Data Science is one among them, to deal with such a massive data volume. This system aids in keeping tabs on a patient's health by recording relevant information. 

The use of Data Science in medicine has made it feasible to spot the first signs of illness in otherwise healthy people. Doctors may now check up on their patients from afar thanks to a host of cutting-edge technology. 

Historically, hospitals and their staffs have struggled to care for large numbers of patients simultaneously. The patients' ailments used to worsen because of a lack of adequate care.

A) Medical Image Analysis:  Focusing on the efforts connected to the applications of computer vision, virtual reality, and robotics to biomedical imaging challenges, Medical Image Analysis offers a venue for the dissemination of new research discoveries in the area of medical and biological image analysis. It publishes high-quality, original research articles that advance our understanding of how to best process, analyze, and use medical and biological pictures in these contexts. Methods that make use of molecular/cellular imaging data as well as tissue/organ imaging data are of interest to the journal. Among the most common sources of interest for biomedical image databases are those gathered from: 

• Magnetic resonance 
• Ultrasound 
• Computed tomography 
• Nuclear medicine 
• X-ray 
• Optical and Confocal Microscopy 
• Video and range data images 

Procedures such as identifying cancers, artery stenosis, and organ delineation use a variety of different approaches and frameworks like MapReduce to determine ideal parameters for tasks such as lung texture categorization. Examples of these procedures include: 

• The categorization of solid textures is accomplished by the use of machine learning techniques, support vector machines (SVM), content-based medical picture indexing, and wavelet analysis. 

B) Drug Research and Development:  The ever-increasing human population brings a plethora of new health concerns. Possible causes include insufficient nutrition, stress, environmental hazards, disease, etc. Medical research facilities now under pressure to rapidly discover treatments or vaccinations for many illnesses. It may take millions of test cases to uncover a medicine's formula since scientists need to learn about the properties of the causal agent. Then, once they have a recipe, researchers must put it through its paces in a battery of experiments.

Previously, it took a team of researchers 10–12 years to sift through the information of the millions of test instances stated above. However, with the aid of Data Science's many medical applications, this process is now simplified. It is possible to process data from millions of test cases in a matter of months, if not weeks. It's useful for analyzing the data that shows how well the medicine works. So, the vaccine or drug may be available to the public in less than a year if all tests go well. Data Science and machine learning make this a reality. Both have been game-changing for the pharmaceutical industry's R&D departments. As we go forward, we shall see Data Science's use in genomics. Data analytics played a crucial part in the rapid development of a vaccine against the global pandemic Corona-virus.

C) Genomics and Bioinformatics:  One of the most fascinating parts of modern medicine is genomics. Human genomics focuses on the sequencing and analysis of genomes, which are made up of the genetic material of living organisms. Genealogical studies pave the way for cutting-edge medical interventions. Investigating DNA for its peculiarities and quirks is what genomics is all about. It also aids in determining the link between a disease's symptoms and the patient's actual health. Drug response analysis for a certain DNA type is also a component of genomics research.

Before the development of effective data analysis methods, studying genomes was a laborious and unnecessary process. The human body has millions of chromosomes, each of which may code for a unique set of instructions. However, recent Data Science advancements in the fields of medicine and genetics have simplified this process. Analyzing human genomes now takes much less time and energy because to the many Data Science and Big Data techniques available. These methods aid scientists in identifying the underlying genetic problem and the corresponding medication.

D) Virtual Assistance:  One excellent illustration of how Data Science may be put to use is seen in the development of apps with the use of virtual assistants. The work of data scientists has resulted in the creation of complete platforms that provide patients with individualized experiences. The patient's symptoms are analyzed by the medical apps that make use of data science in order to aid in the diagnosis of a condition. Simply having the patient input his or her symptoms into the program will allow it to make an accurate diagnosis of the patient's ailment and current status. According on the state of the patient, it will provide recommendations for any necessary precautions, medications, and treatments.

In addition, the software does an analysis on the patient's data and generates a checklist of the treatment methods that must be adhered to at all times. After that, it reminds the patient to take their medication at regular intervals. This helps to prevent the scenario of neglect, which might potentially make the illness much worse. 

Patients suffering from Alzheimer's disease, anxiety, depression, and other psychological problems have also benefited from the usage of virtual aid, since its benefits have been shown to be beneficial. Because the application reminds these patients on a consistent basis to carry out the actions that are necessary, their therapy is beginning to bear fruit. Taking the appropriate medicine, being active, and eating well are all part of these efforts. Woebot, which was created at Stanford University, is an example of a virtual assistant that may help you out. It is a chatbot that assists individuals suffering from psychiatric diseases in obtaining the appropriate therapy in order to improve their mental health. 

4. Data Science Problems In Retail

Although the phrase "customer analytics" is relatively new to the retail sector, the practice of analyzing data collected from consumers to provide them with tailored products and services is centuries old. The development of data science has made it simple to manage a growing number of customers. With the use of data science software, reductions and sales may be managed in real-time, which might boost sales of previously discontinued items and generate buzz for forthcoming releases. One further use of data science is to analyze the whole social media ecosystem to foresee which items will be popular in the near future so that they may be promoted to the market at the same time. 

Data science is far from being complete. loaded with actual uses in the world today. Data science is still in its infancy, but its applications are already being felt throughout the globe. We have a long way to go before we reach saturation.

Steps on How to Approach and Address a Solution to Data Science Problems

Step 1: define the problem.

First things first, it is essential to precisely characterize the data issue that has to be addressed. The issue at hand need to be comprehensible, succinct, and quantifiable. When identifying data scientist challenges, many businesses are far too general with their language, which makes it difficult, if not impossible, for data scientists to transform such problems into machine code. Below we will discuss a few most common data science problem statements and data science challenges. 

The following is a list of fundamental qualities that describe a data issue as well-defined: 

• It seems probable that the solution to the issue will have a sufficient amount of positive effect to warrant the effort. 
• There is sufficient data accessible in a format that can be used. 
• The use of data science as a means of resolving the issue has garnered the attention of stakeholders. 

Step 2: Types of Data Science Problem

There is a wide variety of data science algorithms that can be implemented on data, and they can be classified, to a certain extent, within the following families, below are the most common data science problems examples: 

• Two-class classification: Useful for any issue that can only have two responses, the two-class categorization consists of two distinct categories. 
• Multi-class classification: Providing an answer to a question that might have many different responses is an example of multi-class categorization. 
• Anomaly detection: The term "anomaly detection" refers to the process of locating data points that deviate from the norm. 
• Regression: When searching for a number as opposed to a class or category, regression is helpful since it provides an answer with a real-valued result. 
• Multi-class classification as regression: Useful when questions are posed in the form of rankings or comparisons, multi-class classification may be thought of as regression. 
• Two-class classification as regression: Useful for binary classification problems that can also be reformulated as regression, the two-class classification method is also referred to as regression analysis. 
• Clustering: The term "clustering" refers to the process of answering questions regarding the organization of data by attempting to partition a data set into understandable chunks. 
• Dimensionality reduction: It is the process of acquiring a set of major variables in order to lower the number of random variables that are being taken into account. 
• Reinforcement learning : The goal of the learning algorithms known as reinforcement learning is to perform actions within an environment in such a way as to maximize some concept of cumulative reward.

Step 3: Data Collection

Now that the issue has been articulated in its entirety and an appropriate solution has been chosen, it is time to gather data. It is important to record all of the data that has been gathered in a log, along with the date of each collection and any other pertinent information. 

It is essential to understand that the data produced are rarely immediately available for analysis. The majority of a data scientist's day is dedicated to cleaning the data, which involves tasks such as eliminating records with missing values, locating records with duplicates, and correcting values that are wrong. It is one of the prominent data scientist problems. 

Step 4: Data Analysis

Data analysis comes after data gathering and cleansing. At this point, there is a danger that the chosen data science strategy will fail. This is to be expected and anticipated. In general, it is advisable to begin by experimenting with all of the fundamental machine learning algorithms since they have fewer parameters to adjust. 

There are several good open source data science libraries available for use in data analysis. The vast majority of data science tools are developed in Python, Java, or C++. Apart from this, many data science practice problems are available for free on web. 

Step 5: Result Interpretation

Following the completion of the data analysis, the next step is to interpret the findings. Consideration of whether or not the primary issue has been resolved should take precedence over anything else. It's possible that you'll find out that your model works but generates results that aren't very good. Adding new data and continually retraining the model until one is pleased with it is one strategy for dealing with this situation.

Finalizing the Problem Statement

After identifying the precise issue type, you should be able to formulate a refined problem statement that includes the model's predictions. For instance: 

This is a multi-class classification problem that predicts if a picture belongs to one of four classes: "vehicle," "traffic," "sign," and "human." 

Additionally, you should be able to provide a desired result or intended use for the model prediction. Making a model accurate is one of the most crucial thing Data Scientists problems. 

The optimal result is to offer quick notice to end users when a target class is predicted. One may practice such data science hackathon problem statements on Kaggle. 

Gain the skills you need to excel in business analysis with ccba certification course . Start your journey today and open doors to limitless opportunities!

When professionals are working toward their analytics objectives, they may run across a variety of different kinds of data science challenegs, all of which slow down their progress. The stages that we've discussed in this article on how to tackle a new data science issue are designed to highlight the general problem-solving attitude that businesses need to adopt in order to effectively meet the problems of our present data-centric era.

Not only will a competent data science problem seek to make predictions, but it will also aim to make judgments. Always keep this overarching goal in mind while you think about the many data science challenges you are facing. You may combat the blues of data science with the aid of a detailed approach. In addition, engaging with professionals in the field of data science enables you to get insights, which ultimately results in the effective execution of the project. Have a look at KnowledgeHut’s Data Science Course Subjects to understand this matter in depth.

Frequently Asked Questions (FAQs)

The discipline of data science aims to provide answers to actual challenges faced by businesses by using data in the construction of algorithms and the development of programs that assist in demonstrating that certain issues have ideal solutions. Data science is the use of hybrid mathematical and computer science models to address real-world business challenges in order to get actionable insights. 

Ritesh Pratap Arjun Singh

RiteshPratap A. Singh is an AI & DeepTech Data Scientist. His research interests include machine vision and cognitive intelligence. He is known for leading innovative AI projects for large corporations and PSUs. Collaborate with him in the fields of AI/ML/DL, machine vision, bioinformatics, molecular genetics, and psychology.

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Why every data scientist needs the janitor package.

Posted on August 16, 2024 by Numbers around us in R bloggers | 0 Comments

Lessons from Will Hunting and McGayver

In the world of data science, data cleaning is often seen as one of the most time-consuming and least glamorous tasks. Yet, it’s also one of the most critical. Without clean data, even the most sophisticated algorithms and models can produce misleading results. This is where the janitor package in R comes into play, serving as the unsung hero that quietly handles the nitty-gritty work of preparing data for analysis.

Much like the janitors we often overlook in our daily lives, the janitor package works behind the scenes to ensure everything runs smoothly. It takes care of the small but essential tasks that, if neglected, could bring a project to a halt. The package simplifies data cleaning with a set of intuitive functions that are both powerful and easy to use, making it an indispensable tool for any data scientist.

To better understand the importance of janitor, we can draw parallels to two iconic figures from pop culture: Will Hunting, the genius janitor from Good Will Hunting , and McGayver, the handyman known for his ability to solve any problem with minimal resources. Just as Will Hunting and McGayver possess hidden talents that make a huge impact, the janitor package holds a set of powerful functions that can transform messy datasets into clean, manageable ones, enabling data scientists to focus on the more complex aspects of their work.

Will Hunting: The Genius Janitor

Will Hunting, the protagonist of Good Will Hunting , is an unassuming janitor at the Massachusetts Institute of Technology (MIT). Despite his modest job, Will possesses a genius-level intellect, particularly in mathematics. His hidden talent is discovered when he solves a complex math problem left on a blackboard, something that had stumped even the brightest minds at the university. This revelation sets off a journey that challenges his self-perception and the expectations of those around him.

The story of Will Hunting is a perfect metaphor for the janitor package in R. Just as Will performs crucial tasks behind the scenes at MIT, the janitor package operates in the background of data science projects. It handles the essential, albeit often overlooked, work of data cleaning, ensuring that data is in the best possible shape for analysis. Like Will, who is initially underestimated but ultimately proves invaluable, janitor is a tool that may seem simple at first glance but is incredibly powerful and essential for any serious data scientist.

Without proper data cleaning, even the most advanced statistical models can produce incorrect or misleading results. The janitor package, much like Will Hunting, quietly ensures that the foundations are solid, allowing the more complex and visible work to shine.

McGayver: The Handyman Who Fixes Everything

In your school days, you might have known someone who was a jack-of-all-trades, able to fix anything with whatever tools or materials were on hand. Perhaps this person was affectionately nicknamed “McGayver,” a nod to the famous TV character MacGyver, who was known for solving complex problems with everyday objects. This school janitor, like McGayver, was indispensable — working in the background, fixing leaks, unclogging drains, and keeping everything running smoothly. Without him, things would quickly fall apart.

This is exactly how the janitor package functions in the world of data science. Just as your school’s McGayver could solve any problem with a handful of tools, the janitor package offers a set of versatile functions that can clean up the messiest of datasets with minimal effort. Whether it’s removing empty rows and columns, cleaning up column names, or handling duplicates, janitor has a tool for the job. And much like McGayver, it accomplishes these tasks efficiently and effectively, often with a single line of code.

The genius of McGayver wasn’t just in his ability to fix things, but in how he could use simple tools to do so. In the same way, janitor simplifies tasks that might otherwise require complex code or multiple steps. It allows data scientists to focus on the bigger picture, confident that the foundations of their data are solid.

Problem-Solving with and without janitor

In this section, we’ll dive into specific data cleaning problems that data scientists frequently encounter. For each problem, we’ll first show how it can be solved using base R, and then demonstrate how the janitor package offers a more streamlined and efficient solution.

1. clean_names(): Tidying Up Column Names

Problem: Column names in datasets are often messy — containing spaces, special characters, or inconsistent capitalization — which can make data manipulation challenging. Consistent, tidy column names are essential for smooth data analysis.

Base R Solution: To clean column names manually, you would need to perform several steps, such as converting names to lowercase, replacing spaces with underscores, and removing special characters. Here’s an example using base R:

This approach requires multiple lines of code, each handling a different aspect of cleaning.

janitor Solution: With the janitor package, the same result can be achieved with a single function:

Why janitor Is Better: The clean_names() function simplifies the entire process into one step, automatically applying a set of best practices to clean and standardize column names. This not only saves time but also reduces the chance of making errors in your code. By using clean_names(), you ensure that your column names are consistently formatted and ready for analysis, without the need for manual intervention.

2. tabyl and adorn_ Functions: Creating Frequency Tables and Adding Totals or Percentages

Problem: When analyzing categorical data, it’s common to create frequency tables or cross-tabulations. Additionally, you might want to add totals or percentages to these tables to get a clearer picture of your data distribution.

Base R Solution: Creating a frequency table and adding totals or percentages manually requires several steps. Here’s an example using base R:

This method involves creating tables, adding margins manually, and calculating percentages separately, which can become cumbersome, especially with larger datasets.

janitor Solution: With the janitor package, you can create a frequency table and easily add totals or percentages using tabyl() and adorn_* functions:

Why janitor Is Better: The tabyl() function automatically generates a clean frequency table, while adorn_totals() and adorn_percentages() easily add totals and percentages without the need for additional code. This approach is not only quicker but also reduces the complexity of your code. The janitor functions handle the formatting and calculations for you, making it easier to produce professional-looking tables that are ready for reporting or further analysis.

3. row_to_names(): Converting a Row of Data into Column Names

Problem: Sometimes, datasets are structured with the actual column names stored in one of the rows rather than the header. Before starting the analysis, you need to promote this row to be the header of the data frame.

Base R Solution: Without janitor, converting a row to column names can be done with the following steps using base R:

This method involves manually extracting the row, assigning it as the header, and then removing the original row from the data.

janitor Solution: With janitor, this entire process is streamlined into a single function:

Why janitor Is Better: The row_to_names() function from janitor simplifies this operation by directly promoting the specified row to the header in one go, eliminating the need for multiple steps. This function is more intuitive and reduces the chance of errors, allowing you to quickly structure your data correctly and move on to analysis.

4. remove_constant(): Identifying and Removing Columns with Constant Values

Problem: In some datasets, certain columns may contain the same value across all rows. These constant columns provide no useful information for analysis and can clutter your dataset. Removing them is essential for streamlining your data.

Base R Solution: Identifying and removing constant columns without janitor requires writing a custom function or applying several steps. Here’s an example using base R:

This method involves checking each column for unique values and then filtering out the constant ones, which can be cumbersome.

janitor Solution: With janitor, you can achieve the same result with a simple, one-line function:

Why janitor Is Better: The remove_constant() function from janitor is a straightforward and efficient solution to remove constant columns. It automates the process, ensuring that no valuable time is wasted on writing custom functions or manually filtering columns. This function is particularly useful when working with large datasets, where manually identifying constant columns would be impractical.

5. remove_empty(): Eliminating Empty Rows and Columns

Problem: Datasets often contain rows or columns that are entirely empty, especially after merging or importing data from various sources. These empty rows and columns don’t contribute any useful information and can complicate data analysis, so they should be removed.

Base R Solution: Manually identifying and removing empty rows and columns can be done, but it requires multiple steps. Here’s how you might approach it using base R:

This method involves checking each row and column for completeness and then filtering out those that are entirely empty, which can be cumbersome and prone to error.

janitor Solution: With janitor, you can remove both empty rows and columns in a single, straightforward function call:

Why janitor Is Better: The remove_empty() function from janitor makes it easy to eliminate empty rows and columns with minimal effort. You can specify whether you want to remove just rows, just columns, or both, making the process more flexible and less error-prone. This one-line solution significantly simplifies the task and ensures that your dataset is clean and ready for analysis.

6. get_dupes(): Detecting and Extracting Duplicate Rows

Problem: Duplicate rows in a dataset can lead to biased or incorrect analysis results. Identifying and managing duplicates is crucial to ensure the integrity of your data.

Base R Solution: Detecting and extracting duplicate rows manually can be done using base R with the following approach:

This approach uses duplicated() to identify duplicate rows. While it’s effective, it requires careful handling to ensure all duplicates are correctly identified and extracted, especially in more complex datasets.

janitor Solution: With janitor, identifying and extracting duplicate rows is greatly simplified using the get_dupes() function:

Why janitor Is Better: The get_dupes() function from janitor not only identifies duplicate rows but also provides additional information, such as the number of times each duplicate appears, in an easy-to-read format. This functionality is particularly useful when dealing with large datasets, where even a straightforward method like duplicated() can become cumbersome. With get_dupes(), you gain a more detailed and user-friendly overview of duplicates, ensuring the integrity of your data.

7. round_half_up, signif_half_up, and round_to_fraction: Rounding Numbers with Precision

Problem: Rounding numbers is a common task in data analysis, but different situations require different types of rounding. Sometimes you need to round to the nearest integer, other times to a specific fraction, or you might need to ensure that rounding is consistent in cases like 5.5 rounding up to 6.

Base R Solution: Rounding numbers in base R can be done using round() or signif(), but these functions don't always handle edge cases or specific requirements like rounding half up or to a specific fraction:

While these functions are useful, they may not provide the exact rounding behavior you need in certain situations, such as consistently rounding half values up or rounding to specific fractions.

janitor Solution: The janitor package provides specialized functions like round_half_up(), signif_half_up(), and round_to_fraction() to handle these cases with precision:

Why janitor Is Better: The janitor functions round_half_up(), signif_half_up(), and round_to_fraction() offer more precise control over rounding operations compared to base R functions. These functions are particularly useful when you need to ensure consistent rounding behavior, such as always rounding 5.5 up to 6, or when rounding to the nearest fraction (e.g., quarter or eighth). This level of control can be critical in scenarios where rounding consistency affects the outcome of an analysis or report.

8. chisq.test() and fisher.test(): Simplifying Hypothesis Testing

Problem: When working with categorical data, it’s often necessary to test for associations between variables using statistical tests like the Chi-squared test (chisq.test()) or Fisher’s exact test (fisher.test()). Preparing your data and setting up these tests manually can be complex, particularly when dealing with larger datasets with multiple categories.

Base R Solution: Here’s how you might approach this using a more complex dataset with base R:

This approach involves creating a multidimensional contingency table and then slicing it to apply the tests. This can become cumbersome and requires careful management of the data structure.

janitor Solution: Using janito r , you can achieve the same results with a more straightforward approach:

Why janitor Is Better: The janitor approach simplifies the process by integrating the creation of contingency tables (tabyl()) with the execution of hypothesis tests (chisq.test() and fisher.test()). This reduces the need for manual data slicing and ensures that the data is correctly formatted for testing. This streamlined process is particularly advantageous when dealing with larger, more complex datasets, where manually managing the structure could lead to errors. The result is a faster, more reliable workflow for testing associations between categorical variables.

The Unsung Heroes of Data Science

In both the physical world and the realm of data science, there are tasks that often go unnoticed but are crucial for the smooth operation of larger systems. Janitors, for example, quietly maintain the cleanliness and functionality of buildings, ensuring that everyone else can work comfortably and efficiently. Without their efforts, even the most well-designed spaces would quickly descend into chaos.

Similarly, the janitor package in R plays an essential, yet often underappreciated, role in data science. Data cleaning might not be the most glamorous aspect of data analysis, but it’s undoubtedly one of the most critical. Just as a building cannot function properly without regular maintenance, a data analysis project cannot yield reliable results without clean, well-prepared data.

The functions provided by the janitor package — whether it’s tidying up column names, removing duplicates, or simplifying complex rounding tasks — are the data science equivalent of the work done by janitors and handymen in the physical world. They ensure that the foundational aspects of your data are in order, allowing you to focus on the more complex, creative aspects of analysis and interpretation.

Reliable data cleaning is not just about making datasets look neat; it’s about ensuring the accuracy and integrity of the insights derived from that data. Inaccurate or inconsistent data can lead to flawed conclusions, which can have significant consequences in any field — from business decisions to scientific research. By automating and simplifying the data cleaning process, the janitor package helps prevent such issues, ensuring that the results of your analysis are as robust and trustworthy as possible.

In short, while the janitor package may work quietly behind the scenes, its impact on the overall success of data science projects is profound. It is the unsung hero that keeps your data — and, by extension, your entire analysis — on solid ground.

Throughout this article, we’ve delved into how the janitor package in R serves as an indispensable tool for data cleaning, much like the often-overlooked but essential janitors and handymen in our daily lives. By comparing its functions to traditional methods using base R, we’ve demonstrated how janitor simplifies and streamlines tasks that are crucial for any data analysis project.

The story of Will Hunting, the genius janitor, and the analogy of your school’s “McGayver” highlight how unnoticed figures can make extraordinary contributions with their unique skills. Similarly, the janitor package, though it operates quietly in the background, has a significant impact on data preparation. It handles the nitty-gritty tasks — cleaning column names, removing duplicates, rounding numbers precisely — allowing data scientists to focus on generating insights and building models.

We also explored how functions like clean_names(), tabyl(), row_to_names(), remove_constants(), remove_empty(), get_dupes(), and round_half_up() drastically reduce the effort required to prepare your data. These tools save time, ensure data consistency, and minimize errors, making them indispensable for any data professional.

Moreover, we emphasized the critical role of data cleaning in ensuring reliable analysis outcomes. Just as no building can function without the janitors who maintain it, no data science workflow should be without tools like the janitor package. It is the unsung hero that ensures your data is ready for meaningful analysis, enabling you to trust your results and make sound decisions.

In summary, the janitor package is more than just a set of utility functions — it’s a crucial ally in the data scientist’s toolkit. By handling the essential, behind-the-scenes work of data cleaning, janitor helps ensure that your analyses are built on a solid foundation. So, if you haven’t already integrated janitor into your workflow, now is the perfect time to explore its capabilities and see how it can elevate your data preparation process.

Consider adding janitor to your R toolkit today. Explore its functions and experience firsthand how it can streamline your workflow and enhance the quality of your data analysis. Your data — and your future analyses — will thank you.

Why Every Data Scientist Needs the janitor Package was originally published in Numbers around us on Medium, where people are continuing the conversation by highlighting and responding to this story.

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• Data Science Interview Questions

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Introduction:

Data science is an interdisciplinary field that mines raw data, analyses it, and comes up with patterns that are used to extract valuable insights from it. Statistics, computer science, machine learning, deep learning, data analysis, data visualization, and various other technologies form the core foundation of data science.

Over the years, data science has gained widespread importance due to the importance of data. Data is considered the new oil of the future which when analyzed and harnessed properly can prove to be very beneficial to the stakeholders. Not just this, a data scientist gets exposure to work in diverse domains, solving real-life practical problems all by making use of trendy technologies. The most common real-time application is fast delivery of food in apps such as Uber Eats by aiding the delivery person to show the fastest possible route to reach the destination from the restaurant. 

Data Science is also used in item recommendation systems in e-commerce sites like Amazon, Flipkart, etc which recommend the user what item they can buy based on their search history. Not just recommendation systems, Data Science is becoming increasingly popular in fraud detection applications to detect any fraud involved in credit-based financial applications. A successful data scientist can interpret data, perform innovation and bring out creativity while solving problems that help drive business and strategic goals. This makes it the most lucrative job of the 21st century.

In this article, we will explore what are the most commonly asked Data Science Technical Interview Questions which will help both aspiring and experienced data scientists.

Data Science Interview Questions for Freshers

Data science interview questions for experienced, frequently asked questions, data science mcq, 1. what is data science.

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science.

The following figure represents the life cycle of data science.

• It starts with gathering the business requirements and relevant data.
• Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture.
• Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.
• Once the exploratory steps are completed, the cleansed data is subjected to various algorithms like predictive analysis, regression, text mining, recognition patterns, etc depending on the requirements.
• In the final stage, the results are communicated to the business in a visually appealing manner. This is where the skill of data visualization, reporting, and different business intelligence tools come into the picture. Learn More .

2. Define the terms KPI, lift, model fitting, robustness and DOE.

• KPI: KPI stands for Key Performance Indicator that measures how well the business achieves its objectives.
• Lift: This is a performance measure of the target model measured against a random choice model. Lift indicates how good the model is at prediction versus if there was no model.
• Model fitting: This indicates how well the model under consideration fits given observations.
• Robustness: This represents the system’s capability to handle differences and variances effectively.
• DOE: stands for the design of experiments, which represents the task design aiming to describe and explain information variation under hypothesized conditions to reflect variables.

3. What is the difference between data analytics and data science?

• Data science involves the task of transforming data by using various technical analysis methods to extract meaningful insights using which a data analyst can apply to their business scenarios.
• Data analytics deals with checking the existing hypothesis and information and answers questions for a better and effective business-related decision-making process.
• Data Science drives innovation by answering questions that build connections and answers for futuristic problems. Data analytics focuses on getting present meaning from existing historical context whereas data science focuses on predictive modeling.
• Data Science can be considered as a broad subject that makes use of various mathematical and scientific tools and algorithms for solving complex problems whereas data analytics can be considered as a specific field dealing with specific concentrated problems using fewer tools of statistics and visualization.

The following Venn diagram depicts the difference between data science and data analytics clearly:

4. What are some of the techniques used for sampling? What is the main advantage of sampling?

Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets. It becomes crucial to take some data samples that can be used for representing the whole population and then perform analysis on it. While doing this, it is very much necessary to carefully take sample data out of the huge data that truly represents the entire dataset.

There are majorly two categories of sampling techniques based on the usage of statistics, they are:

• Probability Sampling techniques: Clustered sampling, Simple random sampling, Stratified sampling.
• Non-Probability Sampling techniques: Quota sampling, Convenience sampling, snowball sampling, etc.

5. List down the conditions for Overfitting and Underfitting.

Overfitting: The model performs well only for the sample training data. If any new data is given as input to the model, it fails to provide any result. These conditions occur due to low bias and high variance in the model. Decision trees are more prone to overfitting.

Underfitting: Here, the model is so simple that it is not able to identify the correct relationship in the data, and hence it does not perform well even on the test data. This can happen due to high bias and low variance. Linear regression is more prone to Underfitting.

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6. differentiate between the long and wide format data..

The following image depicts the representation of wide format and long format data:

7. What are Eigenvectors and Eigenvalues?

Eigenvectors are column vectors or unit vectors whose length/magnitude is equal to 1. They are also called right vectors. Eigenvalues are coefficients that are applied on eigenvectors which give these vectors different values for length or magnitude.

A matrix can be decomposed into Eigenvectors and Eigenvalues and this process is called Eigen decomposition. These are then eventually used in machine learning methods like PCA (Principal Component Analysis) for gathering valuable insights from the given matrix.

8. What does it mean when the p-values are high and low?

A p-value is the measure of the probability of having results equal to or more than the results achieved under a specific hypothesis assuming that the null hypothesis is correct. This represents the probability that the observed difference occurred randomly by chance.

• Low p-value which means values ≤ 0.05 means that the null hypothesis can be rejected and the data is unlikely with true null.
• High p-value, i.e values ≥ 0.05 indicates the strength in favor of the null hypothesis. It means that the data is like with true null.
• p-value = 0.05 means that the hypothesis can go either way.

9. When is resampling done?

Resampling is a methodology used to sample data for improving accuracy and quantify the uncertainty of population parameters. It is done to ensure the model is good enough by training the model on different patterns of a dataset to ensure variations are handled. It is also done in the cases where models need to be validated using random subsets or when substituting labels on data points while performing tests.

10. What do you understand by Imbalanced Data?

Data is said to be highly imbalanced if it is distributed unequally across different categories. These datasets result in an error in model performance and result in inaccuracy.

11. Are there any differences between the expected value and mean value?

There are not many differences between these two, but it is to be noted that these are used in different contexts. The mean value generally refers to the probability distribution whereas the expected value is referred to in the contexts involving random variables.

12. What do you understand by Survivorship Bias?

This bias refers to the logical error while focusing on aspects that survived some process and overlooking those that did not work due to lack of prominence. This bias can lead to deriving wrong conclusions.

13. What is a Gradient and Gradient Descent?

Gradient: Gradient is the measure of a property that how much the output has changed with respect to a little change in the input. In other words, we can say that it is a measure of change in the weights with respect to the change in error. The gradient can be mathematically represented as the slope of a function.

Gradient Descent: Gradient descent is a minimization algorithm that minimizes the Activation function. Well, it can minimize any function given to it but it is usually provided with the activation function only. 

Gradient descent, as the name suggests means descent or a decrease in something. The analogy of gradient descent is often taken as a person climbing down a hill/mountain. The following is the equation describing what gradient descent means:

So, if a person is climbing down the hill, the next position that the climber has to come to is denoted by “b” in this equation. Then, there is a minus sign because it denotes the minimization (as gradient descent is a minimization algorithm). The Gamma is called a waiting factor and the remaining term which is the Gradient term itself shows the direction of the steepest descent. 

This situation can be represented in a graph as follows:

Here, we are somewhere at the “Initial Weights” and we want to reach the Global minimum. So, this minimization algorithm will help us do that.

14. Define confounding variables.

Confounding variables are also known as confounders. These variables are a type of extraneous variables that influence both independent and dependent variables causing spurious association and mathematical relationships between those variables that are associated but are not casually related to each other.

15. Define and explain selection bias?

The selection bias occurs in the case when the researcher has to make a decision on which participant to study. The selection bias is associated with those researches when the participant selection is not random. The selection bias is also called the selection effect. The selection bias is caused by as a result of the method of sample collection.

Four types of selection bias are explained below:

• Sampling Bias: As a result of a population that is not random at all, some members of a population have fewer chances of getting included than others, resulting in a biased sample. This causes a systematic error known as sampling bias.
• Time interval: Trials may be stopped early if we reach any extreme value but if all variables are similar invariance, the variables with the highest variance have a higher chance of achieving the extreme value.
• Data: It is when specific data is selected arbitrarily and the generally agreed criteria are not followed.
• Attrition: Attrition in this context means the loss of the participants. It is the discounting of those subjects that did not complete the trial.

16. Define bias-variance trade-off?

Let us first understand the meaning of bias and variance in detail:

Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. When a model is trained, at that time it makes simplified assumptions so that it can easily understand the target function. Some algorithms that have low bias are Decision Trees, SVM, etc. On the other hand, logistic and linear regression algorithms are the ones with a high bias.

Variance: Variance is also a kind of error. It is introduced into an ML Model when an ML algorithm is made highly complex. This model also learns noise from the data set that is meant for training. It further performs badly on the test data set. This may lead to over lifting as well as high sensitivity.

When the complexity of a model is increased, a reduction in the error is seen. This is caused by the lower bias in the model. But, this does not happen always till we reach a particular point called the optimal point. After this point, if we keep on increasing the complexity of the model, it will be over lifted and will suffer from the problem of high variance. We can represent this situation with the help of a graph as shown below:

As you can see from the image above, before the optimal point, increasing the complexity of the model reduces the error (bias). However, after the optimal point, we see that the increase in the complexity of the machine learning model increases the variance.

Trade-off Of Bias And Variance: So, as we know that bias and variance, both are errors in machine learning models, it is very essential that any machine learning model has low variance as well as a low bias so that it can achieve good performance.

Let us see some examples. The K-Nearest Neighbor Algorithm is a good example of an algorithm with low bias and high variance. This trade-off can easily be reversed by increasing the k value which in turn results in increasing the number of neighbours. This, in turn, results in increasing the bias and reducing the variance.

Another example can be the algorithm of a support vector machine. This algorithm also has a high variance and obviously, a low bias and we can reverse the trade-off by increasing the value of parameter C. Thus, increasing the C parameter increases the bias and decreases the variance.

So, the trade-off is simple. If we increase the bias, the variance will decrease and vice versa.

17. Define the confusion matrix?

It is a matrix that has 2 rows and 2 columns. It has 4 outputs that a binary classifier provides to it. It is used to derive various measures like specificity, error rate, accuracy, precision, sensitivity, and recall.

The test data set should contain the correct and predicted labels. The labels depend upon the performance. For instance, the predicted labels are the same if the binary classifier performs perfectly. Also, they match the part of observed labels in real-world scenarios. The four outcomes shown above in the confusion matrix mean the following:

• True Positive: This means that the positive prediction is correct.
• False Positive: This means that the positive prediction is incorrect.
• True Negative: This means that the negative prediction is correct.
• False Negative: This means that the negative prediction is incorrect.

The formulas for calculating basic measures that comes from the confusion matrix are:

• Error rate : (FP + FN)/(P + N)
• Accuracy : (TP + TN)/(P + N)
• Sensitivity = TP/P
• Specificity = TN/N
• Precision = TP/(TP + FP)
• F-Score  = (1 + b)(PREC.REC)/(b2 PREC + REC) Here, b is mostly 0.5 or 1 or 2.

In these formulas:

FP = false positive FN = false negative TP = true positive RN = true negative

Sensitivity is the measure of the True Positive Rate. It is also called recall. Specificity is the measure of the true negative rate. Precision is the measure of a positive predicted value. F-score is the harmonic mean of precision and recall.

18. What is logistic regression? State an example where you have recently used logistic regression.

Logistic Regression is also known as the logit model. It is a technique to predict the binary outcome from a linear combination of variables (called the predictor variables). 

For example , let us say that we want to predict the outcome of elections for a particular political leader. So, we want to find out whether this leader is going to win the election or not. So, the result is binary i.e. win (1) or loss (0). However, the input is a combination of linear variables like the money spent on advertising, the past work done by the leader and the party, etc. 

19. What is Linear Regression? What are some of the major drawbacks of the linear model?

Linear regression is a technique in which the score of a variable Y is predicted using the score of a predictor variable X. Y is called the criterion variable. Some of the drawbacks of Linear Regression are as follows:

• The assumption of linearity of errors is a major drawback.
• It cannot be used for binary outcomes. We have Logistic Regression for that.
• Overfitting problems are there that can’t be solved.

20. What is a random forest? Explain it’s working.

Classification is very important in machine learning. It is very important to know to which class does an observation belongs. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. One such classification technique that is near the top of the classification hierarchy is the random forest classifier. 

So, firstly we need to understand a decision tree before we can understand the random forest classifier and its works. So, let us say that we have a string as given below:

So, we have the string with 5 ones and 4 zeroes and we want to classify the characters of this string using their features. These features are colour (red or green in this case) and whether the observation (i.e. character) is underlined or not. Now, let us say that we are only interested in red and underlined observations. So, the decision tree would look something like this:

So, we started with the colour first as we are only interested in the red observations and we separated the red and the green-coloured characters. After that, the “No” branch i.e. the branch that had all the green coloured characters was not expanded further as we want only red-underlined characters. So, we expanded the “Yes” branch and we again got a “Yes” and a “No” branch based on the fact whether the characters were underlined or not. 

So, this is how we draw a typical decision tree. However, the data in real life is not this clean but this was just to give an idea about the working of the decision trees. Let us now move to the random forest.

Random Forest

It consists of a large number of decision trees that operate as an ensemble. Basically, each tree in the forest gives a class prediction and the one with the maximum number of votes becomes the prediction of our model. For instance, in the example shown below, 4 decision trees predict 1, and 2 predict 0. Hence, prediction 1 will be considered.

The underlying principle of a random forest is that several weak learners combine to form a keen learner. The steps to build a random forest are as follows:

• Build several decision trees on the samples of data and record their predictions.
• Each time a split is considered for a tree, choose a random sample of mm predictors as the split candidates out of all the pp predictors. This happens to every tree in the random forest.
• Apply the rule of thumb i.e. at each split m = p√m = p.
• Apply the predictions to the majority rule.

21. In a time interval of 15-minutes, the probability that you may see a shooting star or a bunch of them is 0.2. What is the percentage chance of you seeing at least one star shooting from the sky if you are under it for about an hour?

Let us say that Prob is the probability that we may see a minimum of one shooting star in 15 minutes.

So, Prob = 0.2

Now, the probability that we may not see any shooting star in the time duration of 15 minutes is = 1 - Prob

1-0.2 = 0.8

The probability that we may not see any shooting star for an hour is: 

= (1-Prob)(1-Prob)(1-Prob)*(1-Prob) = 0.8 * 0.8 * 0.8 * 0.8 = (0.8)⁴   ≈ 0.40

So, the probability that we will see one shooting star in the time interval of an hour is = 1-0.4 = 0.6

So, there are approximately 60% chances that we may see a shooting star in the time span of an hour.

22. What is deep learning? What is the difference between deep learning and machine learning?

Deep learning is a paradigm of machine learning. In deep learning,  multiple layers of processing are involved in order to extract high features from the data. The neural networks are designed in such a way that they try to simulate the human brain. 

Deep learning has shown incredible performance in recent years because of the fact that it shows great analogy with the human brain.

The difference between machine learning and deep learning is that deep learning is a paradigm or a part of machine learning that is inspired by the structure and functions of the human brain called the artificial neural networks. Learn More .

1. How are the time series problems different from other regression problems?

• Time series data can be thought of as an extension to linear regression which uses terms like autocorrelation, movement of averages for summarizing historical data of y-axis variables for predicting a better future.
• Forecasting and prediction is the main goal of time series problems where accurate predictions can be made but sometimes the underlying reasons might not be known.
• Having Time in the problem does not necessarily mean it becomes a time series problem. There should be a relationship between target and time for a problem to become a time series problem.
• The observations close to one another in time are expected to be similar to the ones far away which provide accountability for seasonality. For instance, today’s weather would be similar to tomorrow’s weather but not similar to weather from 4 months from today. Hence, weather prediction based on past data becomes a time series problem.

2. What are RMSE and MSE in a linear regression model?

RMSE: RMSE stands for Root Mean Square Error. In a linear regression model, RMSE is used to test the performance of the machine learning model. It is used to evaluate the data spread around the line of best fit. So, in simple words, it is used to measure the deviation of the residuals.

RMSE is calculated using the formula:

• Yi is the actual value of the output variable.
• Y(Cap) is the predicted value and,
• N is the number of data points.

MSE: Mean Squared Error is used to find how close is the line to the actual data. So, we make the difference in the distance of the data points from the line and the difference is squared. This is done for all the data points and the submission of the squared difference divided by the total number of data points gives us the Mean Squared Error (MSE).

So, if we are taking the squared difference of N data points and dividing the sum by N, what does it mean? Yes, it represents the average of the squared difference of a data point from the line i.e. the average of the squared difference between the actual and the predicted values. The formula for finding MSE is given below:

• Yi is the actual value of the output variable (the ith data point)
• Y(cap) is the predicted value and,
• N is the total number of data points.

So, RMSE is the square root of MSE .

3. What are Support Vectors in SVM (Support Vector Machine)?

In the above diagram, we can see that the thin lines mark the distance from the classifier to the closest data points (darkened data points). These are called support vectors. So, we can define the support vectors as the data points or vectors that are nearest (closest) to the hyperplane. They affect the position of the hyperplane. Since they support the hyperplane, they are known as support vectors.

4. So, you have done some projects in machine learning and data science and we see you are a bit experienced in the field. Let’s say your laptop’s RAM is only 4GB and you want to train your model on 10GB data set.

What will you do have you experienced such an issue before.

In such types of questions, we first need to ask what ML model we have to train. After that, it depends on whether we have to train a model based on Neural Networks or SVM.

The steps for Neural Networks are given below:

• The Numpy array can be used to load the entire data. It will never store the entire data, rather just create a mapping of the data.
• Now, in order to get some desired data, pass the index into the NumPy Array.
• This data can be used to pass as an input to the neural network maintaining a small batch size.

The steps for SVM are given below:

• For SVM, small data sets can be obtained. This can be done by dividing the big data set.
• The subset of the data set can be obtained as an input if using the partial fit function.
• Repeat the step of using the partial fit method for other subsets as well.

Now, you may describe the situation if you have faced such an issue in your projects or working in machine learning/ data science.

5. Explain Neural Network Fundamentals.

In the human brain, different neurons are present. These neurons combine and perform various tasks. The Neural Network in deep learning tries to imitate human brain neurons. The neural network learns the patterns from the data and uses the knowledge that it gains from various patterns to predict the output for new data, without any human assistance.

A perceptron is the simplest neural network that contains a single neuron that performs 2 functions. The first function is to perform the weighted sum of all the inputs and the second is an activation function.

There are some other neural networks that are more complicated. Such networks consist of the following three layers:

• Input Layer: The neural network has the input layer to receive the input.
• Hidden Layer: There can be multiple hidden layers between the input layer and the output layer. The initially hidden layers are used for detecting the low-level patterns whereas the further layers are responsible for combining output from previous layers to find more patterns.
• Output Layer: This layer outputs the prediction.

An example neural network image is shown below:

6. What is Generative Adversarial Network?

This approach can be understood with the famous example of the wine seller. Let us say that there is a wine seller who has his own shop. This wine seller purchases wine from the dealers who sell him the wine at a low cost so that he can sell the wine at a high cost to the customers. Now, let us say that the dealers whom he is purchasing the wine from, are selling him fake wine. They do this as the fake wine costs way less than the original wine and the fake and the real wine are indistinguishable to a normal consumer (customer in this case). The shop owner has some friends who are wine experts and he sends his wine to them every time before keeping the stock for sale in his shop. So, his friends, the wine experts, give him feedback that the wine is probably fake. Since the wine seller has been purchasing the wine for a long time from the same dealers, he wants to make sure that their feedback is right before he complains to the dealers about it. Now, let us say that the dealers also have got a tip from somewhere that the wine seller is suspicious of them.

So, in this situation, the dealers will try their best to sell the fake wine whereas the wine seller will try his best to identify the fake wine. Let us see this with the help of a diagram shown below:

From the image above, it is clear that a noise vector is entering the generator (dealer) and he generates the fake wine and the discriminator has to distinguish between the fake wine and real wine. This is a Generative Adversarial Network (GAN).

In a GAN, there are 2 main components viz. Generator and Discrminator. So, the generator is a CNN that keeps producing images and the discriminator tries to identify the real images from the fake ones. 

7. What is a computational graph?

A computational graph is also known as a “Dataflow Graph”. Everything in the famous deep learning library TensorFlow is based on the computational graph. The computational graph in Tensorflow has a network of nodes where each node operates. The nodes of this graph represent operations and the edges represent tensors.

8. What are auto-encoders?

Auto-encoders are learning networks. They transform inputs into outputs with minimum possible errors. So, basically, this means that the output that we want should be almost equal to or as close as to input as follows. 

Multiple layers are added between the input and the output layer and the layers that are in between the input and the output layer are smaller than the input layer. It received unlabelled input. This input is encoded to reconstruct the input later.

9. What are Exploding Gradients and Vanishing Gradients?

• Exploding Gradients: Let us say that you are training an RNN. Say, you saw exponentially growing error gradients that accumulate, and as a result of this, very large updates are made to the neural network model weights. These exponentially growing error gradients that update the neural network weights to a great extent are called Exploding Gradients .
• Vanishing Gradients: Let us say again, that you are training an RNN. Say, the slope became too small. This problem of the slope becoming too small is called Vanishing Gradient . It causes a major increase in the training time and causes poor performance and extremely low accuracy.

10. What is the p-value and what does it indicate in the Null Hypothesis?

P-value is a number that ranges from 0 to 1. In a hypothesis test in statistics, the p-value helps in telling us how strong the results are. The claim that is kept for experiment or trial is called Null Hypothesis.

• A low p-value i.e. p-value less than or equal to 0.05 indicates the strength of the results against the Null Hypothesis which in turn means that the Null Hypothesis can be rejected. 
• A high p-value i.e. p-value greater than 0.05 indicates the strength of the results in favour of the Null Hypothesis i.e. for the Null Hypothesis which in turn means that the Null Hypothesis can be accepted.

11. Since you have experience in the deep learning field, can you tell us why TensorFlow is the most preferred library in deep learning?

Tensorflow is a very famous library in deep learning. The reason is pretty simple actually. It provides C++ as well as Python APIs which makes it very easier to work on. Also, TensorFlow has a fast compilation speed as compared to Keras and Torch (other famous deep learning libraries). Apart from that, Tenserflow supports both GPU and CPU computing devices. Hence, it is a major success and a very popular library for deep learning.

12. Suppose there is a dataset having variables with missing values of more than 30%, how will you deal with such a dataset?

Depending on the size of the dataset, we follow the below ways:

• In case the datasets are small, the missing values are substituted with the mean or average of the remaining data. In pandas, this can be done by using mean = df.mean() where df represents the pandas dataframe representing the dataset and mean() calculates the mean of the data. To substitute the missing values with the calculated mean, we can use df.fillna(mean) .
• For larger datasets, the rows with missing values can be removed and the remaining data can be used for data prediction.

13. What is Cross-Validation?

Cross-Validation is a Statistical technique used for improving a model’s performance. Here, the model will be trained and tested with rotation using different samples of the training dataset to ensure that the model performs well for unknown data. The training data will be split into various groups and the model is run and validated against these groups in rotation.

The most commonly used techniques are:

• K- Fold method
• Leave p-out method
• Leave-one-out method
• Holdout method

14. What are the differences between correlation and covariance?

Although these two terms are used for establishing a relationship and dependency between any two random variables, the following are the differences between them:

• Correlation: This technique is used to measure and estimate the quantitative relationship between two variables and is measured in terms of how strong are the variables related.
• Covariance: It represents the extent to which the variables change together in a cycle. This explains the systematic relationship between pair of variables where changes in one affect changes in another variable.

Mathematically, consider 2 random variables, X and Y where the means are represented as  μ X {"detectHand":false}  and  μ Y {"detectHand":false}  respectively and standard deviations are represented by  σ X {"detectHand":false}  and  σ Y {"detectHand":false}  respectively and E represents the expected value operator, then:

• covarianceXY = E[(X- μ X {"detectHand":false} ),(Y- μ Y {"detectHand":false} )]
• correlationXY = E[(X- μ X {"detectHand":false} ),(Y- μ Y {"detectHand":false} )]/( σ X {"detectHand":false} σ Y {"detectHand":false} ) so that

Based on the above formula, we can deduce that the correlation is dimensionless whereas covariance is represented in units that are obtained from the multiplication of units of two variables.

The following image graphically shows the difference between correlation and covariance:

15. How do you approach solving any data analytics based project?

Generally, we follow the below steps:

• The first step is to thoroughly understand the business requirement/problem
• Next, explore the given data and analyze it carefully. If you find any data missing, get the requirements clarified from the business.
• Data cleanup and preparation step is to be performed next which is then used for modelling. Here, the missing values are found and the variables are transformed.
• Run your model against the data, build meaningful visualization and analyze the results to get meaningful insights.
• Release the model implementation, and track the results and performance over a specified period to analyze the usefulness.
• Perform cross-validation of the model.

Check out the list of data analytics projects .

16. How regularly must we update an algorithm in the field of machine learning?

We do not want to update and make changes to an algorithm on a regular basis as an algorithm is a well-defined step procedure to solve any problem and if the steps keep on updating, it cannot be said well defined anymore. Also, this brings in a lot of problems to the systems already implementing the algorithm as it becomes difficult to bring in continuous and regular changes. So, we should update an algorithm only in any of the following cases:

• If you want the model to evolve as data streams through infrastructure, it is fair to make changes to an algorithm and update it accordingly.
• If the underlying data source is changing, it almost becomes necessary to update the algorithm accordingly.
• If there is a case of non-stationarity, we may update the algorithm.
• One of the most important reasons for updating any algorithm is its underperformance and lack of efficiency. So, if an algorithm lacks efficiency or underperforms it should be either replaced by some better algorithm or it must be updated.

17. Why do we need selection bias?

Selection Bias happens in cases where there is no randomization specifically achieved while picking a part of the dataset for analysis. This bias tells that the sample analyzed does not represent the whole population meant to be analyzed.

• For example, in the below image, we can see that the sample that we selected does not entirely represent the whole population that we have. This helps us to question whether we have selected the right data for analysis or not.

18. Why is data cleaning crucial? How do you clean the data?

While running an algorithm on any data, to gather proper insights, it is very much necessary to have correct and clean data that contains only relevant information. Dirty data most often results in poor or incorrect insights and predictions which can have damaging effects.

For example, while launching any big campaign to market a product, if our data analysis tells us to target a product that in reality has no demand and if the campaign is launched, it is bound to fail. This results in a loss of the company’s revenue. This is where the importance of having proper and clean data comes into the picture.

• Data Cleaning of the data coming from different sources helps in data transformation and results in the data where the data scientists can work on.
• Properly cleaned data increases the accuracy of the model and provides very good predictions.
• If the dataset is very large, then it becomes cumbersome to run data on it. The data cleanup step takes a lot of time (around 80% of the time) if the data is huge. It cannot be incorporated with running the model. Hence, cleaning data before running the model, results in increased speed and efficiency of the model.
• Data cleaning helps to identify and fix any structural issues in the data. It also helps in removing any duplicates and helps to maintain the consistency of the data.

The following diagram represents the advantages of data cleaning:

19. What are the available feature selection methods for selecting the right variables for building efficient predictive models?

While using a dataset in data science or machine learning algorithms, it so happens that not all the variables are necessary and useful to build a model. Smarter feature selection methods are required to avoid redundant models to increase the efficiency of our model. Following are the three main methods in feature selection:

• These methods pick up only the intrinsic properties of features that are measured via univariate statistics and not cross-validated performance. They are straightforward and are generally faster and require less computational resources when compared to wrapper methods.
• There are various filter methods such as the Chi-Square test, Fisher’s Score method, Correlation Coefficient, Variance Threshold, Mean Absolute Difference (MAD) method, Dispersion Ratios, etc.

• These methods need some sort of method to search greedily on all possible feature subsets, access their quality by learning and evaluating a classifier with the feature.
• The selection technique is built upon the machine learning algorithm on which the given dataset needs to fit.
• Forward Selection: Here, one feature is tested at a time and new features are added until a good fit is obtained.
• Backward Selection: Here, all the features are tested and the non-fitting ones are eliminated one by one to see while checking which works better.
• Recursive Feature Elimination: The features are recursively checked and evaluated how well they perform.
• These methods are generally computationally intensive and require high-end resources for analysis. But these methods usually lead to better predictive models having higher accuracy than filter methods.

• Embedded methods constitute the advantages of both filter and wrapper methods by including feature interactions while maintaining reasonable computational costs.
• These methods are iterative as they take each model iteration and carefully extract features contributing to most of the training in that iteration.
• Examples of embedded methods: LASSO Regularization (L1), Random Forest Importance.

20. During analysis, how do you treat the missing values?

To identify the extent of missing values, we first have to identify the variables with the missing values. Let us say a pattern is identified. The analyst should now concentrate on them as it could lead to interesting and meaningful insights. However, if there are no patterns identified, we can substitute the missing values with the median or mean values or we can simply ignore the missing values. 

If the variable is categorical, the common strategies for handling missing values include:

• Assigning a New Category: You can assign a new category, such as "Unknown" or "Other," to represent the missing values.
• Mode imputation: You can replace missing values with the mode, which represents the most frequent category in the variable.
• Using a Separate Category: If the missing values carry significant information, you can create a separate category to indicate missing values.

It's important to select an appropriate strategy based on the nature of the data and the potential impact on subsequent analysis or modelling.

If 80% of the values are missing for a particular variable, then we would drop the variable instead of treating the missing values.

21. Will treating categorical variables as continuous variables result in a better predictive model?

Yes! A categorical variable is a variable that can be assigned to two or more categories with no definite category ordering. Ordinal variables are similar to categorical variables with proper and clear ordering defines. So, if the variable is ordinal, then treating the categorical value as a continuous variable will result in better predictive models.

22. How will you treat missing values during data analysis?

The impact of missing values can be known after identifying what type of variables have missing values.

• If the data analyst finds any pattern in these missing values, then there are chances of finding meaningful insights.
• In case of patterns are not found, then these missing values can either be ignored or can be replaced with default values such as mean, minimum, maximum, or median values.
• Assigning a new category: You can assign a new category, such as "Unknown" or "Other," to represent the missing values.
• Using a separate category : If the missing values carry significant information, you can create a separate category to indicate the missing values. It's important to select an appropriate strategy based on the nature of the data and the potential impact on subsequent analysis or modelling.
• If 80% of values are missing, then it depends on the analyst to either replace them with default values or drop the variables.

23. What does the ROC Curve represent and how to create it?

ROC (Receiver Operating Characteristic) curve is a graphical representation of the contrast between false-positive rates and true positive rates at different thresholds. The curve is used as a proxy for a trade-off between sensitivity and specificity.

The ROC curve is created by plotting values of true positive rates (TPR or sensitivity) against false-positive rates (FPR or (1-specificity)) TPR represents the proportion of observations correctly predicted as positive out of overall positive observations. The FPR represents the proportion of observations incorrectly predicted out of overall negative observations. Consider the example of medical testing, the TPR represents the rate at which people are correctly tested positive for a particular disease.

24. What are the differences between univariate, bivariate and multivariate analysis?

Statistical analyses are classified based on the number of variables processed at a given time.

25. What is the difference between the Test set and validation set?

The test set is used to test or evaluate the performance of the trained model. It evaluates the predictive power of the model. The validation set is part of the training set that is used to select parameters for avoiding model overfitting.

26. What do you understand by a kernel trick?

Kernel functions are generalized dot product functions used for the computing dot product of vectors xx and yy in high dimensional feature space. Kernal trick method is used for solving a non-linear problem by using a linear classifier by transforming linearly inseparable data into separable ones in higher dimensions.

27. Differentiate between box plot and histogram.

Box plots and histograms are both visualizations used for showing data distributions for efficient communication of information. Histograms are the bar chart representation of information that represents the frequency of numerical variable values that are useful in estimating probability distribution, variations and outliers. Boxplots are used for communicating different aspects of data distribution where the shape of the distribution is not seen but still the insights can be gathered. These are useful for comparing multiple charts at the same time as they take less space when compared to histograms.

28. How will you balance/correct imbalanced data?

There are different techniques to correct/balance imbalanced data. It can be done by increasing the sample numbers for minority classes. The number of samples can be decreased for those classes with extremely high data points. Following are some approaches followed to balance data:

• Specificity/Precision: Indicates the number of selected instances that are relevant.
• Sensitivity: Indicates the number of relevant instances that are selected.
• F1 score: It represents the harmonic mean of precision and sensitivity.
• MCC (Matthews correlation coefficient): It represents the correlation coefficient between observed and predicted binary classifications.
• AUC (Area Under the Curve): This represents a relation between the true positive rates and false-positive rates.

For example, consider the below graph that illustrates training data:

Here, if we measure the accuracy of the model in terms of getting "0"s, then the accuracy of the model would be very high -> 99.9%, but the model does not guarantee any valuable information. In such cases, we can apply different evaluation metrics as stated above.

• Under-sampling This balances the data by reducing the size of the abundant class and is used when the data quantity is sufficient. By performing this, a new dataset that is balanced can be retrieved and this can be used for further modeling.
• Over-sampling This is used when data quantity is not sufficient. This method balances the dataset by trying to increase the samples size. Instead of getting rid of extra samples, new samples are generated and introduced by employing the methods of repetition, bootstrapping, etc.
• Perform K-fold cross-validation correctly: Cross-Validation needs to be applied properly while using over-sampling. The cross-validation should be done before over-sampling because if it is done later, then it would be like overfitting the model to get a specific result. To avoid this, resampling of data is done repeatedly with different ratios. 

29. What is better - random forest or multiple decision trees?

Random forest is better than multiple decision trees as random forests are much more robust, accurate, and lesser prone to overfitting as it is an ensemble method that ensures multiple weak decision trees learn strongly.

30. Consider a case where you know the probability of finding at least one shooting star in a 15-minute interval is 30%. Evaluate the probability of finding at least one shooting star in a one-hour duration?

So the probability is 0.8628 = 86.28%

31. Toss the selected coin 10 times from a jar of 1000 coins. Out of 1000 coins, 999 coins are fair and 1 coin is double-headed, assume that you see 10 heads. Estimate the probability of getting a head in the next coin toss.

We know that there are two types of coins - fair and double-headed. Hence, there are two possible ways of choosing a coin. The first is to choose a fair coin and the second is to choose a coin having 2 heads.

P(selecting fair coin) = 999/1000 = 0.999 P(selecting double headed coin) = 1/1000 = 0.001

Using Bayes rule,

So, the answer is 0.7531 or 75.3%.

32. What are some examples when false positive has proven important than false negative?

Before citing instances, let us understand what are false positives and false negatives.

• False Positives are those cases that were wrongly identified as an event even if they were not. They are called Type I errors.
• False Negatives are those cases that were wrongly identified as non-events despite being an event. They are called Type II errors.

Some examples where false positives were important than false negatives are:

• In the medical field: Consider that a lab report has predicted cancer to a patient even if he did not have cancer. This is an example of a false positive error. It is dangerous to start chemotherapy for that patient as he doesn’t have cancer as starting chemotherapy would lead to damage of healthy cells and might even actually lead to cancer.
• In the e-commerce field: Suppose a company decides to start a campaign where they give $100 gift vouchers for purchasing $10000 worth of items without any minimum purchase conditions. They assume it would result in at least 20% profit for items sold above $10000. What if the vouchers are given to the customers who haven’t purchased anything but have been mistakenly marked as those who purchased $10000 worth of products. This is the case of false-positive error.

33. Give one example where both false positives and false negatives are important equally?

In Banking fields: Lending loans are the main sources of income to the banks. But if the repayment rate isn’t good, then there is a risk of huge losses instead of any profits. So giving out loans to customers is a gamble as banks can’t risk losing good customers but at the same time, they can’t afford to acquire bad customers. This case is a classic example of equal importance in false positive and false negative scenarios.

34. Is it good to do dimensionality reduction before fitting a Support Vector Model?

If the features number is greater than observations then doing dimensionality reduction improves the SVM (Support Vector Model).

35. What are various assumptions used in linear regression? What would happen if they are violated?

Linear regression is done under the following assumptions:

• The sample data used for modeling represents the entire population.
• There exists a linear relationship between the X-axis variable and the mean of the Y variable.
• The residual variance is the same for any X values. This is called homoscedasticity
• The observations are independent of one another.
• Y is distributed normally for any value of X.

Extreme violations of the above assumptions lead to redundant results. Smaller violations of these result in greater variance or bias of the estimates.

36. How is feature selection performed using the regularization method?

The method of regularization entails the addition of penalties to different parameters in the machine learning model for reducing the freedom of the model to avoid the issue of overfitting. There are various regularization methods available such as linear model regularization, Lasso/L1 regularization, etc. The linear model regularization applies penalty over coefficients that multiplies the predictors. The Lasso/L1 regularization has the feature of shrinking some coefficients to zero, thereby making it eligible to be removed from the model.

37. How do you identify if a coin is biased?

To identify this, we perform a hypothesis test as below: According to the null hypothesis, the coin is unbiased if the probability of head flipping is 50%. According to the alternative hypothesis, the coin is biased and the probability is not equal to 500. Perform the below steps:

• Flip coin 500 times
• Calculate p-value.
• p-value > alpha: Then null hypothesis holds good and the coin is unbiased.
• p-value < alpha: Then the null hypothesis is rejected and the coin is biased.

38. What is the importance of dimensionality reduction?

The process of dimensionality reduction constitutes reducing the number of features in a dataset to avoid overfitting and reduce the variance. There are mostly 4 advantages of this process:

• This reduces the storage space and time for model execution.
• Removes the issue of multi-collinearity thereby improving the parameter interpretation of the ML model.
• Makes it easier for visualizing data when the dimensions are reduced.
• Avoids the curse of increased dimensionality.

39. How is the grid search parameter different from the random search tuning strategy?

Tuning strategies are used to find the right set of hyperparameters. Hyperparameters are those properties that are fixed and model-specific before the model is tested or trained on the dataset. Both the grid search and random search tuning strategies are optimization techniques to find efficient hyperparameters.

• Here, every combination of a preset list of hyperparameters is tried out and evaluated.
• The search pattern is similar to searching in a grid where the values are in a matrix and a search is performed. Each parameter set is tried out and their accuracy is tracked. after every combination is tried out, the model with the highest accuracy is chosen as the best one.
• The main drawback here is that, if the number of hyperparameters is increased, the technique suffers. The number of evaluations can increase exponentially with each increase in the hyperparameter. This is called the problem of dimensionality in a grid search.

• In this technique, random combinations of hyperparameters set are tried and evaluated for finding the best solution. For optimizing the search, the function is tested at random configurations in parameter space as shown in the image below.
• In this method, there are increased chances of finding optimal parameters because the pattern followed is random. There are chances that the model is trained on optimized parameters without the need for aliasing.
• This search works the best when there is a lower number of dimensions as it takes less time to find the right set.

Conclusion:

Data Science is a very vast field and comprises many topics like Data Mining, Data Analysis, Data Visualization, Machine Learning, Deep Learning, and most importantly it is laid on the foundation of mathematical concepts like Linear Algebra and Statistical analysis. Since there are a lot of pre-requisites for becoming a good professional Data Scientist, the perks and benefits are very big. Data Scientist has become the most sought job role these days. 

Looking for a comprehensive course on Data Science: Check out Scaler’s Data Science Course .

Useful Resources:

• Best Data Science Courses
• Python Data Science Interview Questions
• Google Data Scientist Salary
• Spotify Data Scientist Salary
• Data Scientist Salary
• Data Science Resume
• Data Analyst: Career Guide
• Tableau Interview
• Additional Technical Interview Questions

1. How do I prepare for a data science interview?

Some of the preparation tips for data science interviews are as follows:

• Resume Building: Firstly, prepare your resume well. It is preferable if the resume is only a 1-page resume, especially for a fresher. You should give great thought to the format of the resume as it matters a lot. The data science interviews can be based more on the topics like linear and logistic regression, SVM, root cause analysis, random forest, etc. So, prepare well for the data science-specific questions like those discussed in this article, make sure your resume has a mention of such important topics and you have a good knowledge of them. Also, please make sure that your resume contains some Data Science-based Projects as well. It is always better to have a group project or internship experience in the field that you are interested to go for. However, personal projects will also have a good impact on the resume. So, your resume should contain at least 2-3 data science-based projects that show your skill and knowledge level in data science. Please do not write any such skill in your resume that you do not possess. If you are just familiar with some technology and have not studied it at an advanced level, you can mention a beginner tag for those skills.
• Prepare Well: Apart from the specific questions on data science, questions on Core subjects like Database Management systems (DBMS), Operating Systems (OS), Computer Networks(CN), and Object-Oriented Programming (OOPS) can be asked from the freshers especially. So, prepare well for that as well.
• Data structures and Algorithms are the basic building blocks of programming. So, you should be well versed with that as well.
• Research the Company: This is the tip that most people miss and it is very important. If you are going for an interview with any company, read about the company before and especially in the case of data science, learn which libraries the company uses, what kind of models are they building, and so on. This gives you an edge over most other people.

2. Are data science interviews hard?

An honest reply will be “YES”. This is because of the fact that this field is newly emerging and will keep on emerging forever. In almost every interview, you have to answer many tough and challenging questions with full confidence and your concepts should be strong to satisfy the interviewer. However, with great practice, anything can be achieved. So, follow the tips discussed above and keep practising and learning. You will definitely succeed.

3. What are the top 3 technical skills of a data scientist?

The top 3 skills of a data scientist are:

• Mathematics: Data science requires a lot of mathematics and a good data scientist is strong in it. It is not possible to become a good data scientist if you are weak in mathematics.
• Machine Learning and Deep Learning : A data scientist should be very skilled in Artificial Intelligence technologies like deep learning and machine learning. Some good projects and a lot of hands-on practice will help in achieving excellence in that field.
• Programming: This is an obvious yet the most important skill. If a person is good at programming it does mean that he/she can solve complex problems as that is just a problem-solving skill. Programming is the ability to write clean and industry-understandable code. This is the skill that most freshers slack because of the lack of exposure to industry-level code. This also improves with practice and experience. 

4. Is data science a good career?

Yes, data science is one of the most futuristic and great career fields. Today and tomorrow or even years later, this field is just going to expand and never end. The reason is simple. Data can be compared to gold today as it is the key to selling everything in the world. Data scientists know how to play with this data to generate some tremendous outputs that are not even imaginable today making it a great career.

5. Are coding questions asked in data science interviews?

Yes, coding questions are asked in data science interviews. One more important thing to note here is that the data scientists are very good problem solvers as they are indulged in a lot of strict mathematics-based activities. Hence, the interviewer expects the data science interview candidates to know data structures and algorithms and at least come up with the solutions to most of the problems.

6. Is python and SQL enough for data science?

Yes. Python and SQL are sufficient for the data science roles. However, knowing the R programming Language can have also have a better impact. If you know these 3 languages, you have got the edge over most of the competitors. However, Python and SQL are enough for data science interviews.

7. What are Data Science tools?

There are various data science tools available in the market nowadays. Various tools can be of great importance. Tensorflow is one of the most famous data science tools. Some of the other famous tools are BigML, SAS (Statistical Analysis System), Knime, Scikit, Pytorch, etc.

Which among the below is NOT a necessary condition for weakly stationary time series data?

Overfitting more likely occurs when there is a huge data amount to train. True or False?

Given the information that the demand is 100 in October 2020, 150 in November 2020, 350 during December 2020 and 400 during January 2021. Calculate a 3-month simple moving average for February 2021.

Which of the below method depicts hierarchical data in nested format?

Which among the following defines the analysis of data objects not complying with general data behaviour?

What does a linear equation having 3 variables represent?

What would be the formula representation of this problem in terms of x and y variables: “The price of 2 pens and 1 pencil as 10 units”?

Which among the below is true regarding hypothesis testing?

What are the model parameters that are used to build ML models using iterative methods under model-based learning methods?

What skills are necessary for a Data Scientist?

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22 . B+ Tree in Data Structure

23 . Expression Tree

24 . Adjacency Matrix

25 . Spanning Tree in Data Structure

26 . Kruskal Algorithm

27 . Prim's Algorithm in Data Structure

28 . Bellman Ford Algorithm

29 . Ford-Fulkerson Algorithm

30 . Trie Data Structure

31 . Floyd Warshall Algorithm

32 . Rabin Karp Algorithm

33 . What Is Dynamic Programming?

34 . Longest Common Subsequence

35 . Fractional Knapsack Problem

36 . Greedy Algorithm

37 . Longest Increasing Subsequence

38 . Matrix Chain Multiplication

39 . Subset Sum Problem

40 . Backtracking Algorithm

41 . Huffman Coding Algorithm

42 . Tower of Hanoi

43 . Stack vs Heap

44 . Asymptotic Analysis

45 . Binomial Distribution

46 . Coin Change Problem

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Coin Change Problem: A Student's Guide to Dynamic Programming

Introduction, pseudocode of coin change problem, solutions to the coin change problem, coin change problem solution using recursion, coin change problem solution using dynamic programming, the complexity of the coin change problem, applications of the coin change problem, wrapping up.

The coin change problem is a classic algorithmic challenge that involves finding the minimum number of coins needed to make a specific amount of change. This problem has practical applications in various fields, including finance, programming, and optimization. In this blog, we will delve into the details of the coin change problem, explore different approaches to solving it, ways to make coin change, and provide examples for better understanding.

Here is a simple pseudocode representation of the coin-changing problem using dynamic programming:

function coinChange(coins[], amount):

for i from 1 to amount:

dp[i] = Infinity

for coin in coins:

if i - coin >= 0:

dp[i] = min(dp[i], dp[i - coin] + 1)

return dp[amount] if dp[amount] != Infinity else -1

This pseudocode outlines the coin change dynamic programming approach to solving the coin change problem, where 'coins[]' represents the denominations of coins available, and 'amount' is the target amount for which we need to make a change. The 'dp' array stores the minimum number of coins required to make each amount from 0 to the target amount 'amount'. The outer loop iterates through each amount from 1 to 'amount', while the inner loop iterates through each coin denomination in 'coins[]' to calculate the minimum coins required for each amount.

There are various approaches to solving the coin change problem. Two common methods are recursive solutions and dynamic programming solutions.

Recursive Solution

The recursive solution involves breaking down the problem into smaller subproblems and recursively solving them. Here is how it works:

• Base Case: If the amount to make change for is 0, then no coins are needed, so the function returns 0.
• Recursive Case: For each coin denomination, we calculate the minimum number of coins required to make change for the remaining amount (amount - coin) and add 1 to account for using one coin of that denomination.
• Select Minimum: Among all the possible coin choices, we select the one that results in the minimum number of coins required.

While the recursive solution is straightforward, it can be inefficient due to redundant calculations, especially for larger amounts or coin sets.

Dynamic Programming Solution

The dynamic programming solution optimizes the recursive approach by storing solutions to subproblems in a table (usually an array). This avoids redundant computations and improves efficiency. Here is how it works:

• Initialization: Create an array 'dp' of size (amount + 1) and set dp[0] = 0, indicating that zero coins are needed to make a change for an amount of 0.
• Dynamic Programming Iteration: Iterate from 1 to the target amount ('amount'). For each amount 'i', iterate through each coin denomination. If using that coin results in a smaller number of coins compared to the current value in 'dp[i]', update 'dp[i]' with the minimum value.
• Result: Return dp[amount] as the minimum number of coins needed to make change for the target amount. If dp[amount] is still infinity, it means making a change for that amount is not possible with the given coin denominations.

The dynamic programming solution significantly improves efficiency by avoiding redundant calculations and solving smaller subproblems first, leading to an optimal solution for the entire problem.

The recursive solution for the coin change problem involves defining a recursive function to calculate the minimum number of coins required. Here is an example in Python:

def coinChangeRec(coins, amount):

if amount == 0:

min_coins = float('inf')

if amount - coin >= 0:

coins_needed = coinChangeRec(coins, amount - coin) + 1

min_coins = min(min_coins, coins_needed)

return min_coins

In this recursive solution:

• The coinChangeRec function takes two arguments: coins, representing the available coin denominations, and amount, representing the target amount for which we need to make a change.
• If the amount is 0, it means no more change is required, so the function returns 0 coins.
• Otherwise, for each coin denomination in coins, the function recursively calculates the minimum number of coins needed to make a change for the remaining amount (amount - coin). It adds 1 to this value to account for using one coin of that denomination.
• The function keeps track of the minimum coins required (min_coins) among all possible coin choices.
• Finally, the function returns the minimum number of coins needed to make a change for the target amount.

The dynamic programming solution optimizes the recursive approach by storing solutions to subproblems in a table. Here is an example in Python:

def coinChangeDP(coins, amount):

dp = [float('inf')] * (amount + 1)

for i in range(1, amount + 1):

return dp[amount] if dp[amount] != float('inf') else -1

In this dynamic programming solution:

• The coinChangeDP function takes two arguments: coins, representing the available coin denominations, and amount, representing the target amount for which we need to make a change.
• It initializes an array dp of size amount + 1 and sets all elements to float('inf') except for dp[0], which is set to 0 because no coins are needed to make change for amount = 0.
• The solution uses a bottom-up approach, iterating from 1 to the amount. For each amount i, it iterates through each coin denomination in coins.
• If using the current coin denomination (coin) results in a smaller number of coins compared to the current value in dp[i], it updates dp[i] with the minimum value (dp[i - coin] + 1).
• Finally, the function returns dp[amount] as the minimum number of coins needed to make a change for the target amount. If dp[amount] is still float('inf'), it means making change for that amount is not possible with the given coin denominations, so it returns -1.

The time complexity of the dynamic programming solution for the coin change problem is O(amount * n), where 'amount' is the target amount and 'n' is the number of coin denominations. The space complexity is also O(amount).

Code Implementation of the Coin Change Problem

Here is a complete Python implementation for the coin change problem:

def coinChange(coins, amount):

The coin change problem has several applications across various domains due to its nature of optimizing resources and finding the minimum number of coins needed to make change for a given amount. Here are some notable applications:

Financial Transactions:

• Making changes in vending machines, cash registers, and ATMs.
• Optimizing currency exchange by using the minimum number of bills and coins.

Resource Allocation:

• Allocating resources efficiently in supply chain management, such as minimizing the number of trucks needed to transport goods by optimizing cargo weight distribution.
• Optimizing inventory management by calculating the minimum number of items needed to fulfill orders.

Algorithm Design:

• As a foundational problem in computer science and algorithms, it is used to teach and practice dynamic programming and recursive techniques.
• Formulating and solving other optimization problems, such as knapsack problems and scheduling problems, that require finding optimal combinations.

Data Structures:

• Designing efficient data structures like priority queues and heap data structures, where the coin change problem can be used as a subproblem for operations like extracting minimum elements.
• Optimizing memory usage and time complexity in algorithms that involve resource allocation.

Gaming and Puzzle Solving:

• Designing game mechanics that involve resource management and optimization, such as coin collection games or puzzle-solving games that require finding optimal solutions.
• Creating mathematical puzzles and challenges that test problem-solving skills.

Optimization Problems:

• Solving optimization problems in various fields, including operations research, economics, and engineering, where minimizing resource usage or maximizing efficiency is crucial.
• Implementing efficient algorithms for load balancing, routing, and task scheduling in distributed systems and networks.

Educational Purposes:

• Teaching algorithmic thinking and problem-solving skills in computer science and mathematics courses.
• Providing practice problems and challenges in programming competitions and hackathons.

These applications indicate the versatility and importance of the coin change problem in various real-world scenarios and computational challenges.

The coin-change problem is a fundamental and versatile computational challenge with applications across diverse domains. Its ability to optimize resources by finding the minimum number of coins needed to make change for a given amount makes it valuable in financial transactions, resource allocation, algorithm design, data structures, gaming, optimization problems, and educational contexts.

Whether in currency exchange optimization, inventory management, algorithm design, puzzle solving, or teaching problem-solving skills; the coin change problem shows us how important it is to use resources efficiently and think algorithmically about real-world contexts and theories.

Its solutions, including dynamic programming and recursive approaches, offer insights into algorithmic optimization and computational efficiency, making it a cornerstone problem in computer science, mathematics, and problem-solving disciplines.

1. What is the coin-changing problem?

The coin-changing problem is a classic computational problem which involves finding the minimum number of coins (of various denominations) needed to make a change for a given amount of money. The goal is to optimize the use of coins and minimize the total number of coins required for the change.

2. What is the coin change problem Knapsack?

The coin change problem Knapsack is a variant of the traditional coin change problem combined with the Knapsack problem. In this variant, along with finding the minimum number of coins to make change for a given amount, there are constraints on the total weight or value of coins that can be used, similar to items that can be placed in a knapsack with limited capacity.

3. What is the minimum coin change problem?

The minimum coin change problem is another name for the traditional coin-changing problem. It refers to the objective of minimizing the number of coins used to make change for a specific amount, considering different denominations of coins.

4. What is the formula for the change-making problem?

The formula for the change-making problem involves dynamic programming techniques. It can be represented as follows:

Let dp[i] represent the minimum number of coins needed to make change for amount i.

Base Case: dp[0] = 0, as no coins are needed to make change for 0 amount.

Recursive Case: For each coin denomination coin, dp[i] = min(dp[i], dp[i - coin] + 1) if i - coin >= 0.

Abhimita Debnath

Abhimita Debnath is one of the students in UpGrad Big Data Engineering program with BITS Pilani. She's a Senior Software Engineer in Infosys. She… Read More

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