DS_Review_Guide

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[![DOI](https://zenodo.org/badge/1141428173.svg)](https://doi.org/10.5281/zenodo.18363614)

# Data Science Review Guide

A comprehensive technical refresher covering statistical foundations and machine learning concepts for data science interviews, compiled over ~80 hours of preparation.

## What This Is

An in-depth review document I created while preparing for data science interviews. It covers hypothesis testing, regression methods, causal inference, Bayesian methods, time series, survival analysis, and machine learning algorithms with enough rigor for me to find it understandable but short enough that I could practically review it during down time.

## What This Isn't

+ A substitute for formal coursework in statistics, probability theory, or statistical methods
+ A comprehensive interview preparation resource (it focuses heavily on technical concepts)
+ Necessarily the right approach for your interviews

## How I Used This

### Study Guide Creation (~80 hours over 1.5 weeks)

+ Identified relevant topics and appropriate depth for my target roles (research-heavy positions)
+ Focused on my strength: distilling technical concepts clearly and understanding root concepts/intuitions/assumptions 
+ Your mileage may vary based on your background and target positions

### Interview Practice (~15 additional hours per round)

+ Created AI agents using this guide and other resources I put together as context to generate interview questions and evaluate responses
+ Iterated through ~100 synthetic questions per interview round
+ Used job descriptions to tailor question difficulty and focus areas

### Coding Prep (minimal)

+ With a CS degree and extensive coding experience, I deprioritized this
+ If you lack similar background, invest time here accordingly

## Citing this resource

If you find any of the information in this guide valuable, or generate resources that derive from material herein, here is a Zenodo citation that can be used:

Eric Bridgeford. (2026). Data Science Review Guide. Zenodo. https://doi.org/10.5281/zenodo.18363615.

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