Data Scientist Mock Interview

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

Question 28 of 30

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

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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.

This is yet another operational question asking you about how you react to a specific situation that may occur during a data analysis exercise. You should be able to answer this question easily as an experienced data scientist. Your answer should address the use of metrics and how they impact the analysis. Knowing this and explaining it to the interviewer will reinforce your qualifications for a data scientist's position.

William Swansen

"The easiest way to address an unbalanced binary classification is to review the metrics you are using in your model. Even though some metrics may be accurate, they may skew the results. Another way you can neutralize this issue is to increase the impact on the analysis for incorrectly classified and any minority class data. This results in a superior model, which produces more accurate results. Another solution is to oversample some of the minority class data or under-sample some of the majority class data, which will balance the binary classification."

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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

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

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?