Useful learning resources (machine learning, data science, and software engineering)¶
Here are some great learning resources that I’ve found helpful.
If you’re interested in jobs in this area, I highly recommend Workera to help figure out what the roles are, what you’re suited to, what you need to improve on, and personalised plans to make this progress.
Machine learning, Coursera, Andrew Ng.
Video lectures, CS229, Standford University.
Computational and Inferential Thinking: The Foundations of Data Science, Ani Adhikari and John DeNero, Data 8: Foundations of Data Science course, UC Berkeley.
Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing, Ron Kohavi, Diane Tang, and Ya Xu.
Causal Diagrams: Draw Your Assumptions Before Your Conclusions, Miguel Hernan, Harvard University.
Causal Inference Book, Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
The Book of Why: The New Science of Cause and Effect, Judea Pearl & Dana Nackenzie, 2019.
Introduction to Causal Inference, Brady Neal.
If you’re interested in jobs in this area, I highly recommend Teach Yourself Computer Science by Oz Nova and Myles Byrne. This will help navigate the key topics and best resources. Many of the resources below are directly taken from this great guide.
Algorithms and Data Structures:
Python Packages, Tomas Beuzen & Tiffany Timbers.
Modern Python Developer’s Toolkit, Sebastian Witowski, PyCon US 2020.
Introduction to Discrete Mathematics for Computer Science Specialization, University of California San Diego HSE University, Coursera.