# Prepping for the Google Product Analyst Interview

Well, it’s certainly been awhile (six years?!) since I’ve written in this blog. The last time I wrote, I was wrapping up my time at the data science bootcamp, Metis. Since then, I’ve worked at Spotify for five years with a short stint at Universal Music Group after that. Now I’m interviewing for the Product Analyst: Data Science role at Google and in preparation for the technical screen, I’d like to write down a little bit about the material I’ve been reviewing.

I plan to go over the following topics:

- Sampling and resampling methods (Bootstrap, cross-validation)
- Hypothesis testing (i.e. binomial proportions)
- Probability theory (conditional probability, expectation, variance)
- Linear & logistic regressions
- Predictive modeling

If time allows, I’ll dive into some more advanced topics such as:

- Bayesian and related methods
- Supervised & unsupervised clustering
- Time series analysis (to be honest this is completely new territory for me)
- Experiment design
- Survival analysis
- Convex optimization (not sure what this is)
- Monte Carlo methods
- Decision trees

Right now I’m primarily using DataCamp to hone my skills, but I plan to expand that to other resources if there’s enough time, including the internal resources given by the recruiter and other websites like Exponent. I plan to keep notes of all my study sessions on this blog for you to follow along in the process, and hopefully guide anyone else who may be interviewing for the same or similar roles. Wish me luck!

–Em