Those are lectures and demonstrations of BigData using several libraries such as pandas, scikit-learn,
mrjob and ipython.
The target audience is experienced Python developers familiar with scientific computing.
To open these notebooks in IPython, download the files to a directory on your computer and from that directory run:
$ ipython notebook
This will open a new page in your browser with a list of the available notebooks.
Use the following links:
- [0-Introduction-to-Pandas](http://nbviewer.ipython.org/urls/raw.github.com/marcelcaraciolo/big-data-tutorial/master/tutorial/0-Introduction-to-Pandas.ipynb)
- [1-Playing-with-recommender-systems](http://nbviewer.ipython.org/urls/raw.github.com/marcelcaraciolo/big-data-tutorial/master/tutorial/1-Playing-with-Recommender-Systems.ipynb)
- [2-Introduction-to-Scikit-Learn](http://nbviewer.ipython.org/urls/raw.github.com/marcelcaraciolo/big-data-tutorial/master/tutorial/2-Introduction-to-Scikit-Learn.ipynb)
- [3-Map-Reduce](http://nbviewer.ipython.org/urls/raw.github.com/marcelcaraciolo/big-data-tutorial/master/tutorial/3-Map-Reduce.ipynb)
This tutorial is distributed under the Creative Commons Attribution 3.0 license. The Python example code and solutions to exercises are distributed under the license Simplified BSD.