This is a collection of links, in various evolving categories, helping one quickly develop insights into and predictions from data.

Starting points

https://github.com/jupyter/jupyter/wiki#statistics-machine-learning-and-data-science

https://nbviewer.org/github/Tanu-N-Prabhu/Python/blob/master/Top_Python_Libraries_Used_In_Data%C2%A0Science.ipynb

Tool tutorials

Tips, trick and accelerators

Alternative tools

Fun projects

Requirements

Data Quality

https://www.ataccama.com/download/dq-analyzer

Worked Example Crib Sheets

Reduce dimensionality by either selecting the most informative features or transforming them into a low-dimensional manifold using dimensionality reduction methods e.g. PCA, LLE, etc. https://machinelearningmastery.com/feature-selection-machine-learning-python/

• feature selection: you select a subset of the original feature set; while
• feature extraction: you build a new set of features from the original feature set.

Predict https://towardsdatascience.com/random-forest-in-python-24d0893d51c0

https://realpython.com/linear-regression-in-python/