I wrote a blog post exactly on that: http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html http://p.migdal.pl/2016/03/15/data-science-i

I wrote a blog post exactly on that: http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html http://p.migdal.pl/2016/03/15/data-science-intro-for-math-ph... (including the data science part). I strongly advice for: - using Python in the interactive environment Jupyter Notebook, - starting with classical machine learning (scikit-learn), NOT from deep learning; first learn logistic regression (a prerequisite for any neural network), kNN, PCA, Random Forest, t-SNE; concepts like log-loss and (cross-)validation, - playing with real data, - it is cool to add neural networks afterwards (here bare TensorFlow is a good choice, but I would suggest Keras). Links: - http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ - http://hangtwenty.github.io/dive-into-machine-learning/ http://hangtwenty.github.io/dive-into-machine-learning/ - https://github.com/leriomaggio/deep-learning-keras-euroscipy2016 https://github.com/leriomaggio/deep-learning-keras-euroscipy...