A Data Science Journey
Curriculum
Twelve tracks from software engineer to data scientist — with live progress.
Algorithms
Python solutions, explicit tradeoffs, and the patterns behind the problems.
Data Wrangling
NumPy arrays first, then pandas — messy data in, analysis-ready tables out.
Statistics
Distributions, uncertainty, and the summaries that make raw data legible.
Videos
Courses worth the time, with the useful parts kept close.
Data Sourcing
Start with the data you can legally use: proprietary data your organization controls, public data open to everyone, or data purchased under license.
Some resources:
U.S. national government
Start with federal datasets published for public use.
U.S. state government
State portals expose regional records that federal datasets often miss.
European
Use the European Union portal for datasets published across Europe.
Non-Profit
Nonprofits publish focused datasets on health, development, and public welfare.
Private organizations
Private organizations release research data and specialized APIs.
Large datasets
These catalogs are built for datasets too large or broad for a single portal.
Web Scraping and APIs
When no dataset exists, collect structured data through an API or scrape it from the web with the right tool:
- import.io
- ScraperWiki
- Tabular
- Google Sheets
- Excel
Google Sheets can import an HTML table directly. Put this formula in cell A1:
=IMPORTHTML('https://en.wikipedia.org/wiki/Iron_Chef_America', 'table', 2)