Ace your next data science interview

Get better at data science interviews by solving a few questions per week

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How it works

1 We write questions

Get relevant questions frequently asked at top companies.

2 You solve them

Solve the problem before receiving the solution the next morning.

3 We send you the solution Premium

Check your work and get better at interviewing!

The schedule

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Sample questions

Sample question: Statistical knowledge

Suppose there are 15 different color crayons in a box. Each time one obtains a crayon, it is equally likely to be any of the 15 types. Compute the expected # of different colors that are obtained in a set of 5 crayons. (Hint: use indicator variables and linearity of expectation)

We enumerate the crayons from 1 to 15. Let \(X_i\) indicate when the ith crayon is among the 5 crayons selected.

\(E(X_i) =\) Pr {Probability that at least one type i crayon is in set of 5}
\(E(X_i) =\) 1 - Pr {no type i crayons in set of 5}
\(E(X_i) = 1 - \frac{14}{15}^5\ \)

Therefore, the expected # of crayons is:

\( = \sum_{i=1}^{25} E(X_i)\)
\( = 15[1 - \frac{14}{15}^5]\)
\( = 4.38\)

Sample question: Coding/computation

Given a dataframe, df, return only those rows which have missing values.
For example:

Name age favorite_color grade name
Willard Morris 20 blue Willard Morris
Al Jennings 19 red 92 Al Jennings
22 yellow 95 Omar Mullins
Spencer McDaniel 21 green 70 Spencer McDaniel

Will return...
Name age favorite_color grade name
Willard Morris 20 blue Willard Morris
22 yellow 95 Omar Mullins

#Written in Python (Pandas)

#First, we build a boolean series of the null values, using 'isnull' and 'any'

#-->df.isnull().any(axis=1) will return the series True, False, True, False

#We can then index this series against our dataframe to filter on the null values



Dylan +

I've been on the mailing list since the initial beta a few months ago, and found the questions to be very helpful with my data science interview at Facebook!

Melissa +

I've been enjoying the mix of questions coming out Data Interview Qs. The balance between stats, data manipulation, classic programming questions, and SQL came in handy during my Amazon interview.

Richard +

Data Interview Qs helped me land a quantitative analyst role at Google. The ROI here is great and would recommend for anyone seeking a role in the data science space.