Python Data Analysis Learning Roadmap (16‑Week Plan)

Python is an object‑oriented, interpreted programming language created by Guido van Rossum in 1989; its simplicity, free open‑source nature, portability, and extensibility have earned it the nickname “glue language,” and its popularity has surged in recent years.

Leveraging Python’s extensive libraries, the article outlines a comprehensive data‑analysis learning roadmap aimed at practitioners, spanning roughly 16 weeks (about 120 days) and divided into four major sections.

1) Python Environment and Basic Syntax – Approximately four weeks are allocated to setting up development tools (e.g., Python(x,y), PyCharm) and mastering core language features, including regular expressions. Essential data‑analysis libraries such as pandas, NumPy, SciPy, and Matplotlib are introduced, with installation typically performed via pip.

2) Data Collection (Web Scraping) – The next four weeks focus on acquiring external data using Python web‑crawling techniques. Numerous free tutorials and video courses are listed, covering topics from basic urllib usage to advanced Scrapy framework configuration.

3) Data Analysis – This segment teaches how to manipulate and analyze data using pandas and NumPy, supported by recommended books and video courses that guide learners through practical exercises and case studies.

4) Data Visualization – The final four weeks concentrate on visualizing data with the Matplotlib library, including 2D, 3D, and map visualizations, supplemented by books and online tutorials.

For each stage, learners are encouraged to select one high‑quality resource and practice extensively; deeper exploration can be pursued independently. Additional tools and references are available on the cited data‑navigation website.

Original article published by the “Data Analysis” (ecshujufenxi) WeChat public account and the “Love Data” navigation site; please credit the source when reproducing.