Run this notebook: Open in Colab Open in Kaggle

Phase 28: Practical Data Science¶

This phase is where you stop sampling topics and start practicing end-to-end work. Treat it like applied training for interviews, take-homes, and portfolio projects.

What This Phase Covers¶

Python data analysis and EDA
Classical ML workflows
Statistics and MLOps thinking
SQL and data engineering basics
Time series, CV, NLP, recommenders, and causal applications

Recommended Order¶

Read README.md in this folder
Complete 00_INTERVIEW_PREP.ipynb
Choose one role-aligned subtrack instead of trying every folder at once
Build one capstone-quality project with a clear metric and written trade-offs

Good Study Routes¶

ML Engineer: machine-learning/, statistics-mlops/, sql-data-engineering/
Data Scientist: python-data-science/, time-series-forecasting/, recommender-causal/
Applied AI Engineer: deep-learning-nlp/, computer-vision/, machine-learning/

The goal is depth with evidence, not checkbox completion.