Month 1 — Foundations

🎯 Goal of this month

By the end of the month you will know:

Math foundations (linear algebra, calculus, statistics) in the ML context
Efficient processing of vectors and matrices with NumPy
Analysis of real data with Pandas
Data visualization with Matplotlib/Seaborn
Final: writing a complete EDA (Exploratory Data Analysis) report on a real dataset

Weekly breakdown

Week	Topic	Time
Week 1	Math foundations + NumPy	8-12 hours
Week 2	Pandas (Series, DataFrame, groupby)	8-12 hours
Week 3	Matplotlib + Seaborn	6-10 hours
Week 4	EDA Capstone project	10-15 hours

Chapter order

Math foundations — Linear algebra, calculus, statistics
NumPy — Fast vector/matrix operations
Pandas — Working with tabular data
Matplotlib and Seaborn — Visualization
EDA Project (Capstone) — Complete practical project
Exercises — Collection of exercises on all topics

What can you do after this month?

Read and analyze any tabular dataset on Kaggle
Convert backend JSON data into a DataFrame and produce statistics
Handle the “data wrangling” that takes 60-80% of ML project time
Draw beautiful charts for client reports

Tip for Backend Dev

Your advantage — working with JSON, dict, list. Imagine Pandas DataFrame as an “in-memory PostgreSQL table”:

df.head() ≈ SELECT * FROM table LIMIT 5
df.groupby('col').sum() ≈ SELECT col, SUM(...) FROM table GROUP BY col
df.merge(df2) ≈ JOIN
df.query("age > 30") ≈ WHERE age > 30

With this mental model you’ll understand Pandas very quickly.

Start

Start with Math foundations.