Data Science

36 lessons: workflow, EDA, ethics—Python playground + MCQs; then NumPy/Pandas.

Mode: server_script
Practice: Server runner
Lessons: 36 units

Start lesson 1 → Introduction to Data Science

Before you start

The end-to-end data science workflow: framing questions, auditing quality, exploring distributions, cleaning features, evaluating models, and communicating decisions.

Turning data into action requires methodology beyond app coding—leakage, bias, and metric choice determine whether analysis helps or harms.

Analytics teams, product experimentation, research labs, and ML-adjacent engineering in every data-driven company.

Python playground lessons (stdlib previews) plus narrative case studies and MCQs; install Jupyter, pandas, and scikit-learn locally for full notebooks.

After /python/intro and basic /sql/intro literacy—before specializing on /numpy/intro and /pandas/intro.

Lesson order

Sequential — follow top to bottom

36 lessons are live in this track. Start from step 01 for the smoothest path.