People have asked me many times for data science resources so I've curated some here about topics that I find particularly important. I'm an economist by background so my interests here also cover cognitive biases and statistical inference (moreso than machine learning).
This isn't an 'awesome' list... it will always be much shorter than that. It's more a curation of reference material, which I think would have a broad audience.
- 🗺 Approaching projects: general resources on data science projects and workflows, e.g. reproducibility.
- 📊 Statistics: resources for learning statistics across different levels of knowledge.
- 📈 Analytics: e.g. coding libraries, data visualisation.
- 🤖 Machine learning: resources for learning & applying machine learning across different levels of knowledge.
- 🤔 Inference: resources for making inferences from data, e.g. causal inference.
- 🧠 Biases: information on cognitive biases and statistical biases that can be problematic with data analysis and research.
- 🐍🏗 Python development: general resources for learning Python language and developing (e.g. Python packaging).
🚧 This is a work in progress. 🚧