The repository of the DataHack Jerusalem 2015 Cisco team

Where to start:

• http://docs.continuum.io/anaconda/index Anaconda might be your safest bet to install everything together.
• It’s a full package and environment manager, read http://conda.pydata.org/docs/intro.html http://conda.pydata.org/docs/test-drive.html
• or you can use pip (http://pip.readthedocs.org/en/stable/installing/) and then use it as a package manager with the whl files from http://www.lfd.uci.edu/~gohlke/pythonlibs/
• http://pydata.org/downloads/ useful links to many of the libraries

Some must haves:

• Numpy
• http://www.numpy.org/
• Scipy
• http://scipy.org/
• Pandas
• http://pandas.pydata.org/
• http://pandas.pydata.org/pandas-docs/stable/10min.html
• http://pandas.pydata.org/pandas-docs/stable/tutorials.html
• Scikit-learn
• http://scikit-learn.org/stable/tutorial/basic/tutorial.html
• http://scikit-learn.org/stable/user_guide.html
• http://scikit-learn.org/stable/auto_examples/index.html
• Matplotlib
• http://matplotlib.org/index.html
• http://matplotlib.org/gallery.html
• IPython / Jupyter
• http://jupyter.org/
• http://nbviewer.ipython.org/github/ipython/ipython/blob/3.x/examples/Notebook/Index.ipynb
• http://nbviewer.ipython.org/ - all sorts of example notebooks
• Bokeh
• http://bokeh.pydata.org/en/latest/
• http://bokeh.pydata.org/en/latest/docs/gallery.html
• Have a look at D3.js too
• http://d3js.org/
• https://github.com/mbostock/d3/wiki/Gallery

• http://datahack-il.com/data/
• http://datahack-il.com/challenges/
• http://archive.ics.uci.edu/ml/index.html - all sorts of classic datasets
• https://www.kaggle.com/competitions (look at the completed ones too!)
• https://github.com/caesar0301/awesome-public-datasets - like the name implies
• http://scikit-learn.org/stable/datasets/index.html#datasets
• http://mldata.org/repository/data/
• http://mlcomp.org/

• http://cisco.safaribooksonline.com/book/software-engineering-and-development/9781783988365 - scikit-learn focus, very good
• http://cisco.safaribooksonline.com/book/programming/python/9781783555130 - new one, a little advanced, but very practical
• http://cisco.safaribooksonline.com/book/programming/python/9781783981960 - pandas focus
• http://cisco.safaribooksonline.com/book/programming/python/9781449323592 - data analysis (pandas and more) focus
• http://cisco.safaribooksonline.com/book/programming/python/9781784392772 - 2nd edition of one of the more famous books, lots of examples
• http://cisco.safaribooksonline.com/book/programming/python/9781785280429 - broad array of data science topics
• http://cisco.safaribooksonline.com/book/programming/python/9781783284818 - IPython, visualizations
• http://cisco.safaribooksonline.com/book/information-technology-and-software-development/9781783989485 - somewhat more advanced scikit-learn ‘cookbook’
• http://cisco.safaribooksonline.com/book/programming/python/9781782161400 - yet another one, which I have as PDF
• http://cisco.safaribooksonline.com/book/programming/machine-learning/9781449330514 - ditto

• https://www.udacity.com/course/intro-to-descriptive-statistics--ud827 - this part covers the more basic stuff I think we probably know quite well
• https://www.udacity.com/course/intro-to-inferential-statistics--ud201 – this is a continuation of the previous course and covers more advanced material which I didn’t know too well like z-tests, t-tests, hypothesis testing etc.
• https://www.udacity.com/course/how-to-use-git-and-github--ud775 – very well done but rather basic introduction to Git and GitHub
• https://www.udacity.com/course/data-visualization-and-d3js--ud507 – I just started but the topic is IMHO very interesting and important
• https://www.udacity.com/course/intro-to-data-science--ud359 – the curriculum is pretty standard by now for this topic in MOOCs, haven’t really tried it out yet.
• https://www.udacity.com/course/intro-to-machine-learning--ud120 - didn’t try it out yet

And then there are the videos of the course that accompanies the excellent ISL book: http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/ http://www-bcf.usc.edu/~gareth/ISL/

And this is a great post covering someone else’s favorite resources: http://www.dataschool.io/how-to-get-better-at-data-science/