Data Scientist Mock Interview

Practice 30 Data Scientist interview questions covering machine learning, statistical modeling, and technical problem-solving.

Question 1 of 30

What statistical software programs do you have experience using in past positions in this field? Which one do have you the most experience with or feel the most confident using?

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William Swansen

William Swansen has worked in the employment assistance realm since 2007. He is an author, job search strategist, and career advisor who helps individuals worldwide and in various professions to find their ideal careers.

"The primary software tools I use for statistical analysis are Tableau, Statgraphics, and JMP. Each of these performs similar functions, but each also offers unique features that make specific types of analysis easier and more accurate. My experience has taught me that once you are familiar with the basic functions of statistical analysis software, you can move between different tools by simply learning the user interface and the functions each one offers. I'm curious about which tools your organization prefers and why."

William Swansen

This is a technical question, the purpose of which is to determine your familiarity with and knowledge of the software used by data scientists. The interviewer is also interested in learning if you are adept at working with the software and tools their organization utilizes. The best way to answer this question is to first state the names of the software you have used and are familiar with, and then explain that most statistical analysis software products have similar features and that you've been able to easily transition from one to another when necessary.

William Swansen

"In my current position, we use Tableau for the majority of our work. We also have licenses for Statgraphics and JMP Statistical Software, but these are only used in circumstances in which their unique features are more suited for the task at hand. I've also used Salesforce Analytics Cloud and MATLAB in previous roles. I've found that transitioning to a new software analytics tool is relatively easy due to the similarity in the features and user interface between the different packages."

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30Data Scientist Interview Questions & Answers

Technical

1.What statistical software programs do you have experience using in past positions in this field? Which one do have you the most experience with or feel the most confident using?

Technical

2.What programming languages do you have experience using? Of these, which do you have the most experience with? Which do you have the least experience with?

General

3.To be a successful Data Scientist, many in the industry believe it is important to stay up-to-date on the newest technologies and methodologies. What new data-related technology/methodology have you heard of that you wish you could learn more about?

General

4.The work of a Data Scientist can have a large impact on the strategy, and ultimately success, of a business. Is there a time you felt your work as a Data Scientist had a profound impact on strategy development? Explain your role and contribution.

General

5.When your job requires you to be immersed in data, you can discover some interesting patterns or trends. What is the most interesting discovery you made through the mining/exploration of data?

Behavioral

6.Describe to me a data project you worked on in the past that you would do differently with the knowledge/experience you have acquired up to this point and/or new technology that was not available at the original time of the project.

Technical

7.What data visualization tools do you have experience using? Which one is your favorite to use and why?

Behavioral

8.Describe a time when you had to present findings/recommendations to a non-technical audience. What strategies did you use to ensure the audience did not get confused and clearly understood the message?

Operational

9.Describe to me your experience with machine learning methods. Is there a particular method you have more experience with than others?

General

10.In your past positions, have you had experience contributing to the improvement of data analysis processes, database management, data infrastructure, or anything along those lines? If so, please explain your contributions.

Operational

11.Data visualization is an important skill that will be used often when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.

Technical

12.What experience do you have conducting text analytics? Describe a project you worked on that required text analytics.

General

13.How have past positions unrelated to data analysis helped you in your current profession as a Data Scientist?

Behavioral

14.Many companies rely on Data Scientists to tell them what analysis is possible with the data available. Talk about a time when you took the initiative to recommend a new business measure for the company to track.

Behavioral

15.Describe a project where you had a surprisingly difficult time dealing with unstructured data. How did you overcome the obstacles and what tools did you use?

Behavioral

16.Data Scientists do a lot of exploring and testing of hypotheses. Tell me about a time you were given freedom to explore a business problem with very few parameters. What was your initial approach in attacking this project?

Technical

17.Can you compare Sas, R, and Python programming tools and describe their use in Data Analytics?

Operational

18.As a Data Scientist, how do you employ statistics to analyze data and develop business recommendations?

Technical

19.What is Data Cleansing and why is it important in Data Analysis?

Operational

20.Can you describe how Data Analysis is used by businesses and other organizations?

Operational

21.Can you describe some of the steps you take to ensure that a regression model fits the data?

Technical

22.What is a decision tree, and how do you use this in your job as a data scientist?

Operational

23.Do you follow the hypothesis that many small decision trees are more accurate than one large one?

Operational

24.In your opinion, is mean square error a good or bad measure of model performance?

Operational

25.What are some of the assumptions required to accurately perform a linear regression analysis?

Operational

26.Do you perform data wrangling and data cleaning before applying machine learning algorithms to your data analysis?

Technical

27.Can you discuss some of the weaknesses of a linear analysis model?

Operational

28.How do you deal with an unbalanced binary classification when analyzing a data set?

Technical

29.What are some of the differences between a histogram and a box plot?

Technical

Data Scientist Mock Interview

What statistical software programs do you have experience using in past positions in this field? Which one do have you the most experience with or feel the most confident using?

30Data Scientist Interview Questions & Answers

1.What statistical software programs do you have experience using in past positions in this field? Which one do have you the most experience with or feel the most confident using?

2.What programming languages do you have experience using? Of these, which do you have the most experience with? Which do you have the least experience with?

3.To be a successful Data Scientist, many in the industry believe it is important to stay up-to-date on the newest technologies and methodologies. What new data-related technology/methodology have you heard of that you wish you could learn more about?

4.The work of a Data Scientist can have a large impact on the strategy, and ultimately success, of a business. Is there a time you felt your work as a Data Scientist had a profound impact on strategy development? Explain your role and contribution.

5.When your job requires you to be immersed in data, you can discover some interesting patterns or trends. What is the most interesting discovery you made through the mining/exploration of data?

6.Describe to me a data project you worked on in the past that you would do differently with the knowledge/experience you have acquired up to this point and/or new technology that was not available at the original time of the project.

7.What data visualization tools do you have experience using? Which one is your favorite to use and why?

8.Describe a time when you had to present findings/recommendations to a non-technical audience. What strategies did you use to ensure the audience did not get confused and clearly understood the message?

9.Describe to me your experience with machine learning methods. Is there a particular method you have more experience with than others?

10.In your past positions, have you had experience contributing to the improvement of data analysis processes, database management, data infrastructure, or anything along those lines? If so, please explain your contributions.

11.Data visualization is an important skill that will be used often when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.

12.What experience do you have conducting text analytics? Describe a project you worked on that required text analytics.

13.How have past positions unrelated to data analysis helped you in your current profession as a Data Scientist?

14.Many companies rely on Data Scientists to tell them what analysis is possible with the data available. Talk about a time when you took the initiative to recommend a new business measure for the company to track.

15.Describe a project where you had a surprisingly difficult time dealing with unstructured data. How did you overcome the obstacles and what tools did you use?

16.Data Scientists do a lot of exploring and testing of hypotheses. Tell me about a time you were given freedom to explore a business problem with very few parameters. What was your initial approach in attacking this project?

17.Can you compare Sas, R, and Python programming tools and describe their use in Data Analytics?

18.As a Data Scientist, how do you employ statistics to analyze data and develop business recommendations?

19.What is Data Cleansing and why is it important in Data Analysis?

20.Can you describe how Data Analysis is used by businesses and other organizations?

21.Can you describe some of the steps you take to ensure that a regression model fits the data?

22.What is a decision tree, and how do you use this in your job as a data scientist?

23.Do you follow the hypothesis that many small decision trees are more accurate than one large one?

24.In your opinion, is mean square error a good or bad measure of model performance?

25.What are some of the assumptions required to accurately perform a linear regression analysis?

26.Do you perform data wrangling and data cleaning before applying machine learning algorithms to your data analysis?

27.Can you discuss some of the weaknesses of a linear analysis model?

28.How do you deal with an unbalanced binary classification when analyzing a data set?

29.What are some of the differences between a histogram and a box plot?

30.Can you define cross-validation and describe how you use this process when analyzing a data set?