Theory and Concepts
This is a collection of theoretical background knowledge needed for hands-on projects. Each track can be studied independently, and itโs a great place to look up concepts as you progress through projects.
๐ผ 1. Pandas
From the basics to advanced techniques of data manipulation. Learn DataFrame internals and the efficiency of vectorized operations.
Learn the Pandas Track โ
๐๏ธ 2. SQL
Understand SQLโs core mechanisms including the difference between query writing order and execution order, and set theory-based JOINs.
Learn the SQL Track โ
๐ 3. Visualization
Covers data visualization techniques using Matplotlib, Seaborn, and Plotly, along with methods for choosing effective charts.
Learn the Visualization Track โ
๐ 4. Statistics
Learn essential statistical theory for data analysis including hypothesis testing (T-test), A/B testing, and regression analysis with formulas.
Learn the Statistics Track โ
๐ค 5. Machine Learning
Dive deep into the principles of ML algorithms from unsupervised learning (Clustering) to supervised learning (Classification/Regression).
Learn the Machine Learning Track โ