Track 01
Time Series
Decompose and test time series in Python: STL, ADF and KPSS stationarity tests, white noise and random walks.
Data science · Machine learning · AI
Learn data science and AI, one notebook at a time.
Step-by-step learning tracks built from runnable Jupyter notebooks. Pick a track, start at Part 1, and work forward: every part builds on the one before it.
Learning roadmap
Follow a track, in order
Each track is a numbered series of notebooks. The roadmap shows what each part covers and which to read next.
No matching items
Track 02
Python
Learn Python from the ground up: data types, control flow, functions, collections, files, exceptions, OOP and web basics.
No matching items
Latest
Recently published
Oct 18, 2026
White Noise and Random Walks in Time Series Analysis
Learn how to identify White Noise and Random Walk processes in financial time series using visual, ACF/PACF, and Ljung-Box test diagnostics.
Oct 11, 2026
Stationarity in Time Series Analysis
Learn how to detect non-stationarity in time series using statistical tests and apply techniques like differencing, transformations, and detrending to achieve stationarity.
Oct 4, 2026
Time Series Analysis and Decomposition
Learn the fundamentals of time series forecasting, additive vs. multiplicative models, and robust STL decomposition using NIFTY 50 data.
Oct 20, 2024
Python Basics: Variables and Data Types
An introduction to Python, its interpreters, and core data types: numbers, strings, lists, tuples, dictionaries and basic operators.
Oct 20, 2024
Python Control Flow: if, elif, else and Loops
Learn how Python makes decisions and repeats work with if, elif and else statements, for and while loops, and break and continue.
No matching items
