A cultivated list of resources I used to get started and stay current on data science topics — mainly for R and Python users, with some stats and CS fundamentals. It worked for me; your mileage may vary based on background.
Courses
- Intro to Data Science in Python — Coursera
- Intro to Data Science in R — Coursera
- R Programming — Coursera
- Data Science Toolkit — theory + some application
- Chicago DS MLOps
Books
- ISLR — the bible. Free, available for Python or R. If you read one book, read this.
- Python Machine Learning — Sebastian Raschka. The one I still keep at my desk.
- Python Data Science Handbook — VanderPlas. Free online, great for getting up to speed on pandas/numpy.
- Advanced R — Hadley Wickham. The recommended reading list in the intro alone is worth it.
- Build a Career in Data Science — Nolis & Thompson. Great on the industry/career side.
- Machine Learning Yearning — Andrew Ng. Free PDF.
- Lantz ML in R — super approachable intro to ML concepts with R code.
- Large repo of free DS PDFs — assembled by Jimmy Oty and Lawrence Juma.
Job Boards
- Tech Jobs For Good — mission-based tech jobs, full range of seniority.
- Ben Green's Job List — data science in social good sectors.
- aijobs.net, datajobs, outerjoin — internship and remote-focused.
Blogs & Other
- Towards Data Science — daily emails of digestible technical content. Probably my most-used resource.
- Anna-Lena Popkes — excellent Python walkthroughs of ML concepts.
- Youyang Gu — did extraordinary COVID modeling during the pandemic. Excellent writer.
- Marginal Revolution — not DS-specific, but Tyler Cowen is one of the most interesting thinkers writing publicly. His podcast is also great.
- CV Done Wrong — quick read on cross-validation. Good refresher.