Nick Singh | The Data Science Guy πŸ“•

Nick Singh | The Data Science Guy πŸ“•



Google recently released an "Interview Warmup" tool, to help candidates who are applying to Data Analytics jobs at Google better prep for their technical interview. Here's 15 Data Analytics Questions Google expects you to know: (how many can you answer??)

1. Not all data or inputs for analysis arrive in perfect condition. Explain some of the errors or problems you might look for as part of the data cleaning process.

2. Some large companies store data in data centers in multiple countries. Why does it matter which countries your data is stored in?

3. Analysts often need to combine data sets from different sources using joins. Can you describe the common types of joins you may need to complete? 4. Can you describe what a subquery is in SQL?

5. You're putting together a presentation for a client using several data visualizations. What steps would you take in your final review of the presentation to make sure the information is clearly communicated?

6. When would choose to use a programming language like R instead of spreadsheets or SQL? 7. Please give an example of a question that is better asked of a focus group, and an example that is better asked using a survey.

8. You are joining data with phone numbers as an identifier and you find that people have entered their phone numbers with different formats: Some with dashes, some with parentheses, and some with spaces. What do you do?

9. I calculated the mean age of the people in my data using a programming language, and the answer came back as N/A. What might have happened, and what could I do to solve this?

10. Given a dataset, you are tasked with creating a visualization that shows the relationships between two variables. When would you prefer to use a scatterplot, and when would you prefer to use a heatmap?

11. Ensuring data is free from bias is an essential part of creating valid insights. What are some sources of bias that you should consider when evaluating data sources?

12. When presenting information to a client, what are some key differences between reports and dashboards? Can you think of an example where a dashboard would be preferred over a report?

13. "If the sample is large enough, we don't need to worry about whether it's biased." Do you agree with the statement? Why, or why not?

14. Can you explain why primary and foreign keys are an important part of database management? 15. Can you describe what metadata is, and why it's important in the discussion of databases?

How many of these could you answer? Comment your score below, I'm curious! Also be sure to follow me @NickSinghTech for more data interview tips! Also go practice Google SQL interview questions on And finally, please RT the tweet below ❀️

And if you need 6 FREE Data Science / Data Analyst prep resources, check this thread out:

Also did you notice that #SQL comes up SO MUCH during data interviews. Want to learn SQL for FREE? Here's the 30 Day SQL Roadmap:

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