A Complete Guide to Data Science Interviews – Tips & Frequently Asked Questions

Data science is one such field in which the demand for professionals is often more than the number of professionals actually available. With solid packages, exciting work projects, and the opportunity to build a sterling career, data science has become a top and sought-after domain. 

Data science has been a buzzword in the world of technology for some time now. The generation of huge amounts of data and the evolution of technology have created a high demand for data scientists all across the world. The importance of collecting and processing data plays a huge role as it allows organizations to identify and influence the trends in their industry. 

If you too wish to explore the domain and want to start preparing for your data science interview, then this article is for you. Keep reading below to learn about the data science interview tips, frequently asked questions, and other useful information that will help you crack a data science interview. 

Generally, data science pairs with other breakthrough technologies like Machine Learning (ML), Internet of Things (IoT), and Artificial Intelligence (AI). The advancements in the development of these technologies and data science have increased their impact across all industries. 

Whether you have just completed your data science course or artificial intelligence course, and looking for some great career opportunities as a data scientist, thisinterview guide is sure to help you. 

Career Scope in Data Science 

With millions of job opportunities in big data, the role of a data scientist has become more important than ever. In fact, it won’t be wrong to say that it is the most sought-after job at present and will be the same at least for a decade. Yes, that’s true!! 

In today’s data-centric world, organizations are using detailed insights provided by data scientists to gain an edge over their competitors while keeping their overhead head costs as low as possible. Big tech giants like Apple, Oracle, Microsoft, Walmart, Booz Allen, and Hamilton have a plethora of job openings for data scientists on a regular basis.

Moreover, LinkedIn states that there are roughly around 218,250 data scientist job openings available at this moment. What’s more? Data scientists are considered to be the fastest-growing careers and have also ranked in the top 50 jobs in the USA. 

What we can say from this data is that you can secure a bright career in the rising field of data science, provided you have the right knowledge, qualifications and experience. Although there are hundreds of data science courses and data science certifications available, you need to select the best if you really want to build your career as a data scientist. 

So, this is about the career opportunities after completing the best data science courses. Now, before moving on to the interview questions, let’s discuss how data science is connected to machine learning and artificial intelligence! 

Are Machine Learning and Artificial Intelligence Techniques Important for Data Science Jobs? 

How much are AI and ML concepts used in data science jobs? Or, Should I also take an artificial intelligence course or a machine learning course alongside a data science course? These are some of the biggest questions that come to the mind of anyone preparing for a career in data science.

In fact, it is one of the hottest debate topics among beginners. However, the truth is that both artificial intelligence and machine learning are deeply connected with data science.

Today, data scientists play a huge role in the development and growth of AI. They create powerful algorithms for learning correlations and patterns from data, which artificial intelligence uses to curate predictive models that produce useful insights from the given data. Further, data scientists also use AI as a tool to understand data and make informed business decisions. The same goes for machine learning. Data scientists use it for automating repetitive tasks.

In concert, data science along with AI and ML make predictive analytics hassle-free. It helps data scientists to predict consumer behavior and allow retail services to serve them in a better way through advanced delivery systems and inventory management.

Why Should Data Scientists Have Machine Learning and Artificial Intelligence Skills?

Data scientists create predictive algorithms, analyze statistical models, test and enhance machine learning model efficiency, gain insights, use data visualization, and finally communicate the results to the stakeholders. Below are the applications of ML and AI used by data scientists:

• Descriptive Analytics – ML algorithms such as K-means clustering are used.
• Diagnostic Analytics – Decision Tree is enough to serve the purpose. 
• Predictive Analytics – Support Vector Machine algorithm or regression algorithm is used.
• Prescriptive Analytics – AI, ML, and neural network algorithms serve the purpose.

As you can see, data science, ML, and AI are intricately linked to each other. Most of the decisions made by data scientists are based on both of these technologies. Hence, understanding the basics of both AI and ML is of utmost importance for growing as a data scientist. 

Now that we have answered all your doubts, it’s time to finally discuss the most asked data science interview questions and their answers. Let’s get into it! 

Top 10 Data Science Interview Questions and Answers

1. How will you define logistic regression in data science?

Logistic Regression or the Logit model is a method of forecasting the binary outcome from a linear combination of one or more independent variables or predictors. 

2. What are the three biases that may occur during sampling? 

Selection Bias, Survivorship Bias, and Under Coverage Bias are the three biases that can occur at the time of sampling. 

3. Define the Decision Tree algorithm.

Decision Tree is one of the most popular and useful machine learning algorithms. It is primarily used for Classification and Regression. It allows us to break down large datasets into smaller subsets. Further, it can handle both numerical and categorical data. 

4. What is a Recommender System? 

A recommender system is a subclass of the information filtering mechanism. It allows you to predict the ratings that customers are most likely to give to a product. 

5. Are there any disadvantages of using a linear model? 

Yes, there are mainly three downsides of the linear model:

• We can’t use it for the count of binary outcomes. 
• It cannot solve a multitude of overfitting problems. 
• Assumption of linearity of errors is another disadvantage of using a linear model. 

5. When do you perform resampling? 

Resampling is performed in the following cases:

• For validating models using any random subsets 
• For substituting labels on data points while performing required tests 
• For estimating the accuracy of sample statistics by randomly drawing with replacement from a dataset or subsets of accessible data. 

6. Name a few python libraries that are used for scientific computations and data analysis. 

• NumPy
• SciPy
• SciKit
• Matplotlib 
• Seaborn 

7. What do you mean by the Naive Bayes algorithm?

The Naive Bayes algorithm is used to describe the probability of an event. It is based on the Bayes Theorem and prior knowledge of conditions related to that particular event. 

8. Discuss Linear Regression.

Linear Regression is a method of statistical programming where the score of a variable ‘X’ is predicted from the score of another variable ‘Y’. Here, X is known as the criterion variable, and Y is the predictor variable. 

9. Why do we conduct a/b testing? 

A/B testing is used to perform experiments with two variables. The goal of this method is to identify the changes made on a webpage to increase the outcome of a specific strategy. 

10. Define the K-means clustering method.

K-means clustering is an unsupervised learning method. It is used to classify data using a particular set of clusters (known as K clusters). Moreover, it is used for grouping in order to find out the similarity in the data. 

Frequently Asked Technical Data Science Interview Questions 

11. Name the feature selection methods used for selecting the right variables. 

The two main feature selection methods are:

• Filter Methods – These involve Chi-Square, Linear Discrimination Analysis, and ANOVA
• Wrapper Methods – These involve Forward Selection, Backward Selection, and Recursive Feature Elimination  

12. Suppose you are given a dataset of variables that has over 30% missing value. How will you deal with this situation? 

Here are the ways to deal with missing values:

• If the data set is large, then we can simply eliminate the rows that contain missing values. It is the easiest way to deal with missing values. Further, we can predict the values using the remaining data. 
• If there is a smaller data set, then we can substitute the missing values with the average or mean value of the remaining data with the help of the panda’s data frame. We can do so in different ways. For example: using df.fillna(mean) or df.mean().

13. How do you treat outlier values? 

We can remove the outlier only if it is a garbage value. For example: Height of a person =xyz ft. This line can never be true as the height of a person cannot be a string value. In this case, we can eliminate the outliers. 

Further, the outliers can also be dropped if they have extreme values. For example, if all the data points lie between 0 and 10 and there is one point that lies at 100, then we can drop it. 

14. What if you can’t drop the outliers?

In such cases, we can try the following:

• Try different data models 
• Try to normalize the data
• Use algorithms that are least affected by outliers. For example, random forests 

So, these were some of the most common data science interview questions. By preparing these questions, you can sail through the interview session swiftly. But to make sure you land in the best company with the best package, here are some interview preparation tips for you to check out! 

Tips to Prepare for the Data Science Interview 

• Research the job role and identify your capabilities for that job by reading the job description carefully. 
• Try to get an idea of what your interviewer is looking for. This is because some interviewers focus on strong technical skills, while some others pay attention to soft skills and learning abilities. 
• Be very honest about your software experience and technical skills. Do not blindly say YES to everything they ask. 
• Ask questions about the team you’re going to work with (if selected). 
• Be ready to discuss the salary. Be confident and don’t settle for less. You can get an idea of the salary from websites like Salary.com and GlassDoor.
• Don’t hesitate to ask questions to your interviewer. 
• Don’t panic if you can’t answer any questions. Just try to handle it smartly like say, ‘I didn’t get the chance to explore this area yet, can someone on the jury teach me? 

Ready to Crack Your Next Data Science Interview? 

From career opportunities to interview questions and preparation tips, we have discussed everything you need to know before appearing in your data science interview. Data science is the fastest-growing field that is presenting bright career opportunities to the youths. 

All that is required is the right qualification and knowledge which you can easily achieve from our best data science courses like the Integrated Program in Data Science, Machine Learning, and Artificial Intelligence offered in collaboration with MIT and the PG Certificate Program in Business Analytics and Data Science offered in partnership with edX and from Georgia Tech.