Useful resources 🤓

This includes quality online books, courses and video content.

Platform stuff 🐍

• Jupyter (Python) - local install with anaconda | Colab | UCloud - Install, play around 💾

• GitHub - Create an account and follow 101 tutorials 🧑‍💻

• Kaggle - 🧗 Create an account, check out projects and competitions.

Books

• Python Data Science Handbook- he book covers all essentials and includes free online notebooks with code and explanation.

• Econometrics with Python - Causal Inference for The Brave and True

• Fundamentals of Data Visualization

Techniques and skills

• madewithml A good place to start is 👉 madewithml. Built by a former Apple ML Engineer, it brings together most of the topics relevant for Data Science, Machine Learning and MLOps (things we will cover on the 2. semester). Start with 🛠 toolkit (Notebooks to Pandas - no need to look into PyTorch for now). Explore then the 🔥 Machine Learning part - here, only linear and logistic regression as well as data quiality.

• Youtube: Python Engineer Are you really giving us a Youtube Channel here? Well, yes. 😜 Modern research communication, especially in computational disciplines is fast and open. Many engineers and researchers put out excellent contetent - sometimes of higher quality thatn academic publications, especially those that take many years to produce. This channel covers various topic in Data Science and software development more generally. Consider following the advanced python playlist. If you are very motivated, also the ML from scratch playlist.