Week 01 — Data Science Foundations¶
Week 01 builds the working foundation every data scientist needs. By the end of this week you should be able to write clean Python, manipulate data with NumPy and Pandas, create informative charts, reason about statistics, query databases with SQL, and run a structured exploratory data analysis on a real dataset.
What This Week Covers¶
| Day | Part | Topic |
|---|---|---|
| Day 01 | Part 1 | Python Basics — variables, control flow, functions, collections |
| Day 01 | Part 2 | Advanced Python — OOP, file handling, modules, exceptions |
| Day 02 | Part 1 | NumPy — arrays, indexing, vectorization, broadcasting |
| Day 02 | Part 2 | Pandas Basics — Series, DataFrames, filtering, sorting |
| Day 03 | Part 1 | Pandas Advanced — GroupBy, merge, apply, missing values |
| Day 03 | Part 2 | Data Visualization — Matplotlib, Seaborn, best practices |
| Day 04 | Part 1 | Statistics Basics — mean, median, variance, distributions |
| Day 04 | Part 2 | Inferential Statistics — hypothesis tests, p-values, correlation |
| Day 05 | Part 1 | SQL for Data Science — SELECT through window functions |
| Day 05 | Part 2 | EDA — cleaning, outliers, univariate and bivariate analysis |
How to Use This Week¶
Work through each day's notes in order. Every section builds on the previous one. After reading a note, type the code examples yourself — do not copy-paste. Finish the exercises before moving on.
At the end of each day, answer the interview questions out loud. If you cannot explain a concept in plain English, go back and review it.
Start here: Day 01 — Python Basics Agenda