Data Science Interview Preparation
Prepare for SQL, statistics, experimentation, product sense and case rounds with realistic practice
A taste of a lesson
In an interview I said a p value of 0.03 means there's a 3% chance the null hypothesis is true. They didn't look happy. Why?
That's one of the most common misinterpretations. A p value of 0.03 means: if the null hypothesis were true, there'd be a 3% chance of seeing data at least this extreme. It's about the data assuming the null, not the probability that the null is true. To get that you'd need a Bayesian approach with prior beliefs. Try saying the correct version in your own words, then tell me what a p value doesn't tell you about effect size.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Write and explain SQL with joins, aggregations and window functions
- Explain p values, confidence intervals and power correctly and simply
- Design and critique A/B tests including sample size and pitfalls
- Diagnose metric changes and define success metrics for features
- Present analyses with a clear recommendation and honest limitations
Lesson plan
- 1 SQL under interview conditions Solve SQL problems accurately while explaining your logic. Start
- 2 Statistics you can explain Explain core statistical ideas correctly in plain language. Start
- 3 Experiment design Design A/B tests and identify their pitfalls. Start
- 4 Product and metrics sense Define success metrics and diagnose metric changes systematically. Start
- 5 Modelling and take homes Frame modelling problems and deliver take home analyses well. Start
- 6 Presenting to stakeholders Present findings with a clear recommendation for non technical audiences. Start
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About this tutor
For candidates preparing for data scientist and product analytics interviews. We practise the usual rounds: SQL (joins, aggregations, window functions), statistics and probability, A/B testing and experiment design, product and metrics sense, modelling basics, and take home or case presentations. You answer out loud and get structured feedback on correctness, clarity and business thinking. We focus on reasoning that transfers across companies, and on explaining results to non technical stakeholders, which is often what separates candidates.
Reviews
4.7
3 ratingsSample
- Mariana C.Sample
My p value and confidence interval explanations are finally correct and simple. The metric drop framework came up almost word for word in an interview.
- Hana Y.Sample
Presenting with the recommendation first changed how my take home landed. Feedback was honest and useful.
- Deepak R.Sample
Strong on experiments and statistics. SQL practice was good, though I wanted more hard window function problems.
About the teacher
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI roles
9 tutors 243 lessons taught Sample
I work as a machine learning engineer and have spent a lot of my spare time reviewing portfolios and running mock interviews for people trying to get into ML, data and AI product roles. I have sat on both sides of the interview table, so I know what interviewers listen for and how often strong people undersell themselves. I teach...
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