Data Science Interview

After our admissions team has reviewed your application, you will have to complete two rounds of evaluation prior to admittance into our Data Science Immersive program - one Coding Challenge and one Technical Interview. After your interview, we will share an admissions decision within two business days.

During your interview, feel free to ask questions, talk through your process and tell the interviewers when you’re stuck. We’re interested in your approach, ability to think through a problem and current knowledge. There is no penalty for asking for help.

Coding Challenge

The technical exercise is made up of two Python questions. While there is no time limit, this exercise typically takes one to two hours to complete and must be submitted within seven days of receipt. Our online environment allows you to test your code to make sure it’s working before you submit. Once all the tests are passing, you will submit your code and you will be able to schedule your technical interview. If you cannot pass the tests, don’t worry! We have prep options in many formats, including a Python Fundamentals course to get you ready for success next time around.

Technical Interview

The main skill assessed in our 90 minute technical interview is your ability to use Python to express your reasoning in probability/statistics. Using a collaborative code editor, you’ll work through solving a series of problems using Python while an interviewer observes your work. Prior to this interview, you should be familiar with dictionaries, lists, control flow, data types and how to use them in Python. Additionally, it is important that you have a basic understanding of programming principles, experience working on the command line, understand basic flow/logical states and can write simple functions in Python.

The interview will also involve working with data (either real or simulated) and performing some basic statistical analyses on the data. You may, for example, be asked to perform probabilistic calculations, including calculating conditional probabilities and referencing discrete probability distributions (Discrete Uniform, Bernoulli, Binomial, and Poisson). Again, the tools of Python will be at your disposal as you perform these calculations.