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The Open-Source Data Science Masters

The Internet is Your Oyster

I didn't want to wait. I wanted to work on things I care about now. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?

see my transcript

With Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?

The Motivation

We need more Data Scientists.

...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.

-- McKinsey Report Highlights the Impending Data Scientist Shortage 23 July 2013

There are little to no Data Scientists with 5 years experience, because the job simply did not exist.

-- David Hardtke How To Hire A Data Scientist 13 Nov 2012

An Academic Shortfall

Classic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.

Academic credentials are important but not necessary for high-quality data science. The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.

We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available. Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.

And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.

-- James Kobielus, Closing the Talent Gap 17 Jan 2013

Ready?

The Open Source Data Science Curriculum

Start here. Intro to Data Science UW / Coursera

Topics: Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.

Data Science / Harvard Video Archive & Course

Topics: Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.

Data Science with Open Source Tools Book

Topics: Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics
Example Code in: R, Python, Sage, C, Gnu Scientific Library

A Note About Direction

This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). Out of personal preference and need for focus, I geared the original curriculum toward Python tools and resources. R resources can be found here.

Math

Computing

Algorithms
Algorithms Design & Analysis I Stanford / Coursera
Algorithm Design, Kleinberg & Tardos Book $125
Distributed Computing Paradigms
*See Intro to Data Science UW / Lectures on MapReduce
Intro to Hadoop and MapReduce Cloudera / Udacity Course *includes select free excerpts of Hadoop: The Definitive Guide Book $29
Databases
SQL Tutorial w3schools / Tutorials
SQL Tutorial SQLZOO / Tutorials
Introduction to Databases Stanford / Online Course
Data Mining
Mining Massive Data Sets Stanford / Digital & Book $58
Mining The Social Web Book $30
Introduction to Information Retrieval / Stanford Digital & Book $56

OSDSM Specialization: Web Scraping & Crawling

Machine Learning
Machine Learning Ng Stanford / Coursera
A Course in Machine Learning UMD / Digital Book
Programming Collective Intelligence Book $27
The Elements of Statistical Learning / Stanford Digital^ & Book $80
Machine Learning Caltech / Edx
Neural Networks U Toronto / Coursera
Statistical Network Analysis & Modeling
Probabilistic Programming and Bayesian Methods for Hackers Github / Tutorials
Probabalistic Graphical Models Stanford / Coursera
Natural Language Processing
NLP with Python (NLTK library) Digital, Book $36
Analysis
Python for Data Analysis Paper Book $24
Big Data Analysis with Twitter UC Berkeley / Lectures
Social and Economic Networks: Models and Analysis / Stanford / Coursera
Visualization
Envisioning Information (Information Visualization) Tufte / Book $36
Data Visualization, CS 171 Harvard / Lectures
Scott Murray's Tutorial on D3 Blog / Tutorials
Berkely's Viz Class UC Berkeley / Course Docs
Rice University's Data Viz class Rice University

OSDSM Specialization: Data Journalism

Python (Learning)

Learn Python the Hard Way Digital & Book $23
Python Class / Google
Think Python Digital & Book $34
Introduction to Computer Science and Programming MIT OpenCourseWare / Lectures

Python (Libraries)

Installing Basic Packages Python, virtualenv, NumPy, SciPy, matplotlib and IPython & Using Python Scientifically

More Libraries can be found in related specialiaztions

Data Structures & Analysis Packages
- Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc pandas & Tutorials Python for Data Analysis / Book
Machine Learning Packages
- scikit-learn - Tools for Data Mining & Analysis
Networks Packages
- networkx - Network Modeling & Viz
Statistical Packages
- PyMC - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit
- Statsmodels - Python module that allows users to explore data, estimate statistical models, and perform statistical tests
- PyMVPA - Multivariate Pattern Analysis in Python
Natural Language Processing & Understanding
- NLTK - Natural Language Toolkit
- Gensim - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
Live Data Packages
- twython - Python wrapper for the Twitter API
Visualization Packages
- Orange - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining
iPython Data Science Notebooks
Data Science in IPython Notebooks (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)

Datasets are now here

R resources are now here

Capstone Project

Resources

DataTau - The "Hacker News" of Data Science
Coursera
Khan Academy
Metacademy
Wolfram Alpha
Wikipedia
The Signal and The Noise - Nate Silver Pop-Sci Data Analysis $15
Zipfian Academy's List of Resources
A Software Engineer's Guide to Getting Started with Data Science
Data Scientist Interviews Metamarkets
/r/MachineLearning Reddit

Notation

Paid books, courses, and resources are noted with $.

Contribute

Please Contribute Your Ideas -- this is Open Source!

Please showcase your own specialization & transcript by submitting a markdown file pull request in the /transcripts directory with your name! eg clare-corthell-2014.md

Follow me on Twitter @clarecorthell

License

chadchouGitHub/go

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