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.



IPython Notebook(s) demonstrating spark and HDFS functionality.

Notebook Description
spark In-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms.
hdfs Reliably stores very large files across machines in a large cluster.


IPython Notebook(s) demonstrating Hadoop MapReduce with mrjob functionality.

Notebook Description
mapreduce-python Supports MapReduce jobs in Python with 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 is another python-based alternative.


IPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functionality.

Notebook Description
s3cmd Interacts with S3 through the command line.
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 Uploads multiple files to S3 in parallel.
redshift Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP).
kinesis Streams data in real time with the ability to process thousands of data streams per second.
lambda Runs code in response to events, automatically managing compute resources.


IPython Notebook(s) used in kaggle competitions and business analyses.

Notebook Description
titanic Predicts survival on the Titanic. Demonstrates data cleaning, exploratory data analysis, and machine learning.
churn-analysis Predicts customer churn. Exercises logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Discussion of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.


IPython Notebook(s) demonstrating scikit-learn functionality.

Notebook Description
intro 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.
knn K-nearest neighbors.
linear-reg Linear regression.
svm Support vector machine classifier, with and without kernels.
random-forest Random forest classifier and regressor.
k-means K-means clustering.
pca Principal component analysis.
gmm Gaussian mixture models.
validation Validation and model selection.


IPython Notebook(s) demonstrating pandas functionality.

Notebook Description
pandas Software library written for data manipulation and analysis in Python. Offers data structures and operations for manipulating numerical tables and time series.


IPython Notebook(s) demonstrating matplotlib functionality.

Notebook Description
matplotlib Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
matplotlib-applied 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.


IPython Notebook(s) demonstrating NumPy functionality.

Notebook Description
numpy 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.


[Coming Soon] IPython Notebook(s) demonstrating SciPy functionality.


IPython Notebook(s) demonstrating Python functionality geared towards data analysis.

Notebook Description
data structures Tuples, lists, dicts, sets.
data structure utilities Slice, range, xrange, bisect, sort, sorted, reversed, enumerate, zip, list comprehensions.
functions Functions as objects, lambda functions, closures, args, *kwargs currying, generators, generator expressions, itertools.
datetime Datetime, strftime, strptime, timedelta.
logging Logging with RotatingFileHandler and TimedRotatingFileHandler.
pdb Interactive source code debugger.
unit tests Nose unit tests.


IPython Notebook(s) demonstrating various command lines for Linux, Git, etc.

Notebook Description
linux 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 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 Web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document.
git Distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows.
ruby Used to interact with the AWS command line and for Jekyll, a blog framework that can be hosted on GitHub Pages.
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 is a python-based alternative.
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, Flask, Tornado, and Bottle.


IPython Notebook(s) demonstrating miscellaneous functionality.

Notebook Description
regex Regular expression cheat sheet useful in data wrangling.


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 or the more lightweight miniconda.

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.

$ git clone
$ cd [downloaded repo directory name]
$ ipython notebook



Feel free to contact me to discuss any issues, questions, or comments.


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:

Copyright 2015 Donne Martin

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
See the License for the specific language governing permissions and
limitations under the License.