Preparing For Facebook’s Data Science Interview

Read on how to prepare and ace the Data Science interview from our Data Scientist, Lesley Cordero. Lesley describes her experience taking the technical test at leading tech companies, including Facebook and Foursquare.

I have a 20 min technical test for Data Science Intern position. This is my first such interview. What can I expect on the technical test?

I’ve interviewed for many data science positions, including internships, from small start-ups to big companies like Foursquare and Facebook.  The types of questions I’ve encountered have varied a lot, actually.

Sometimes companies will treat their interview process as any other software engineering position and ask traditional coding questions, such as string manipulation, data structures, etc. Websites like HackerRank and LeetCode are awesome online resources that will should properly ramp you up for this kind of interview. Additionally, Cracking the Coding Interview is full of the most commonly asked coding challenges. If you’re interested in face-to-face practice, Byte Academy hosts a regular meetup practicing interview questions based on this book’s tips.

Companies might actually go into questions more related to a Data Science position. For example, questions on regression, Bayes’ Theorem, arithmetic, and distributions commonly come up. Taking a statistics course or two should adequately prepare you for these.

And then there are some companies that feature a mix of the two. To reiterate, it really depends on the company, so making sure you research the company (check Glassdoor) to see what their interview process is like can be the key to knocking these interviews out of the park.

Thanks for the comment
No Comments

Other Suggested Reads

  • The Worst Kind of Data: Missing Data

    Most publicly available datasets or datasets at the workplace are complete. However, from time to time we encounter datasets where some or many entries are missing. The problem of missing data exists ...
  • How to Overcome the Curse of Dimensionality

    Dimensionality reduction is an important technique to overcome the curse of dimensionality in data science and machine learning. As the number of predictors (or dimensions or features) in the dataset ...
  • K-Means Clustering: All You Need to Know

    In machine learning, we are often in the realm of “function approximation”. That is, we have a certain ground-truth (y) and associated variables (X) and our aim is to use identify a function to wr...