Continually updated Data Science IPython Notebooks.
This repo is a collection of IPython Notebooks I reference while working with data. Although I developed and maintain most notebooks, some notebooks I reference were created by other authors, who are credited within their notebook(s) by providing their names and/or a link to their source.
| [spark](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/spark/spark.ipynb) | In-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms. |
| [hdfs](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/spark/hdfs.ipynb) | Reliably stores very large files across machines in a large cluster. |
| [mapreduce-python](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/mapreduce/mapreduce-python.ipynb) | Supports MapReduce jobs in Python with [mrjob](https://github.com/Yelp/mrjob), running them locally or on Hadoop clusters. Demonstrates mrjob code, unit test, and config file to analyze Amazon S3 bucket logs on Elastic MapReduce. [Disco](https://github.com/discoproject/disco/) is another python-based alternative.|
| [s3cmd](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3cmd) | Interacts with S3 through the command line. |
| [s3distcp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3distcp) | Combines smaller files and aggregates them together by taking in a pattern and target file. S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster. |
| [s3-parallel-put](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#s3-parallel-put) | Uploads multiple files to S3 in parallel. |
| [redshift](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#redshift) | Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP). |
| [kinesis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#kinesis) | Streams data in real time with the ability to process thousands of data streams per second. |
| [lambda](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/aws/aws.ipynb#lambda) | Runs code in response to events, automatically managing compute resources. |
| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predicts survival on the Titanic. Demonstrates data cleaning, exploratory data analysis, and machine learning. |
| [intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb) | Intro notebook to scikit-learn. Scikit-learn adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |
| [linear-reg](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-linear-reg.ipynb) | Linear regression. |
| [svm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-svm.ipynb) | Support vector machine classifier, with and without kernels. |
| [random-forest](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-random-forest.ipynb) | Random forest classifier and regressor. |
| [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Validation and model selection. |
| scipy | SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. |
| [pandas](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb) | Software library written for data manipulation and analysis in Python. Offers data structures and operations for manipulating numerical tables and time series. |
| [matplotlib](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb) | Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. |
| [matplotlib-applied](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib-applied.ipynb) | Matplotlib visualizations appied to Kaggle competitions for exploratory data analysis. Examples of bar plots, histograms, subplot2grid, normalized plots, scatter plots, subplots, and kernel density estimation plots. |
| [numpy](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb) | Adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |
| [logging](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/logs.ipynb) | Logging with RotatingFileHandler and TimedRotatingFileHandler. |
| [unit tests](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/unit_tests.ipynb) | Nose unit tests. |
| [linux](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/linux.ipynb) | Unix-like and mostly POSIX-compliant computer operating system. Disk usage, splitting files, grep, sed, curl, viewing running processes, terminal syntax highlighting, and Vim.|
| [anaconda](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#anaconda) | Distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. |
| [ipython notebook](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#ipython-notebook) | Web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document. |
| [git](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#git) | Distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows. |
| [ruby](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#ruby) | Used to interact with the AWS command line and for Jekyll, a blog framework that can be hosted on GitHub Pages. |
| [jekyll](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#jekyll) | Simple, blog-aware, static site generator for personal, project, or organization sites. Renders Markdown or Textile and Liquid templates, and produces a complete, static website ready to be served by Apache HTTP Server, Nginx or another web server. [Pelican](https://github.com/getpelican/pelican) is a python-based alternative. |
| [django](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#django) | High-level Python Web framework that encourages rapid development and clean, pragmatic design. It can be useful to share reports/analyses and for blogging. Lighter-weight alternatives include [Pyramid](https://github.com/Pylons/pyramid), [Flask](https://github.com/mitsuhiko/flask), [Tornado](https://github.com/tornadoweb/tornado), and [Bottle](https://github.com/bottlepy/bottle).
Anaconda is a free distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing that aims to simplify package management and deployment.
Follow instructions to install [Anaconda](http://docs.continuum.io/anaconda/install.html) or the more lightweight [miniconda](http://conda.pydata.org/miniconda.html).
To view interactive content or to modify elements within the IPython notebooks, you must first clone or download the repository then run the ipython notebook. More information on IPython Notebooks can be found [here.](http://ipython.org/notebook.html)
* [Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793) by Wes McKinney
* [PyCon 2015 Scikit-learn Tutorial](https://github.com/jakevdp/sklearn_pycon2015) by Jake VanderPlas
* [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel
This repository contains a variety of content; some developed by Donne Martin, and some from third-parties. The third-party content is distributed under the license provided by those parties.
The content developed by Donne Martin is distributed under the following license:
