FAQ: Prepare for TECHNICAL analytics & data job interviews
I get a lot of questions about how to prepare for job interviews for analytics & data science roles. The process I follow, and what I advise to the people that I coach is:
Organize your past experience (STAR!) — read about this in my last email
Practice the technical stuff
Think about the business
What I do during the actual interview
Originally I was going to send all of this in one email, but it became a very long email, so I’m breaking it into four parts.
Read on for Part Two - how to prepare for the technical interview.
Step One: Figure out what they cover
Live coding challenges are very common for analytics and data science interviews. While it’s a good idea to regularly practice code, usually, the technical assessments come after the recruiter (or even hiring manager) round. You can ask the recruiter what to expect at each stage of the interview, and if they mention a technical assessment, you can ask what specific language(s) and topics are covered. Also ask if there will be any other technical parts - maybe questions around specific topics?
Step Two: Practice technical skills
Once you know what language(s) they’ll test, start doing some practice problems.
Not sure where to actually practice coding? Check out Dataford and Interview Query, they are both geared toward preparing for analytics & data science interviews.
I usually try to do a few practice problems each day leading up to a technical interview. But I also try to stay in the habit of doing a few problems every week even when I don’t have an interview scheduled, just to stay sharp.
Step Three: Other topics
You sometimes need to study other topics besides code. Maybe your recruiter told you there will be experimentation (usually hypothesis testing), probability, or machine learning questions.
Even if the recruiter didn’t give you a heads up about this, if the job description mentions those topics, they are fair game for the interview. I’ve been asked all kinds of math questions during interviews, far too many to list, unfortunately. So it does pay off to review some of the definitions you learned during your studies, such as …
What’s the difference between mean and median and when should you use each?
How do you check for NULL or outliers in your data?
What’s the difference between JOIN and UNION?
(The following questions are typically only asked for data science or advanced analytics roles … but you never know what will come up!)
What’s a p-value? A confidence interval? A stratified sample? And more statistics & probability questions.
What’s precision versus recall versus an f-1 score? How can you tell if your model is overfit? Underfit? What’s the difference between bias and variance? And more machine learning questions.
Step Four: After the interview
I actually have a spreadsheet that I call my “interview cheat sheet.” In it, I have definitions for terms like the ones listed above. I review it before technical interviews.
I also keep a running list of what comes up in each interview - both for technical challenges and other questions. If I struggled during the interview, I know what to study or practice for next time.
Stay tuned next week for Part Three: Get Familiar with the Business.
Need more help? I’m thinking of putting together a job search cohort course - join the waitlist to find out when (if?) it launches!
You can read my answers to past FAQs. Have a question of your own? Reply to this email!
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