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The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.

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

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

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

Start here. Intro to Data ScienceUW / 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 ToolsBook $27

• 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

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.

[★ What are some good resources for learning about numerical analysis? / Quora ] (http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)

• Linear Algebra & Programming

• Linear Algebra / Levandosky Stanford / Book $10

• Linear Programming (Math 407) University of Washington / Course

• Statistics

• Statistics I Princeton / Coursera

• Stats in a Nutshell Book $29

• Think Stats: Probability and Statistics for Programmers Digital & Book $25

• Think Bayes Digital & Book $25

• Differential Equations & Calculus

• Differential Equations in Data Science Python Tutorial

• Problem Solving

• Problem-Solving Heuristics "How To Solve It" Polya / Book $10

Get your environment up and running with the Data Science Toolbox

• 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

• Introduction to Databases Stanford / Online Course

• SQL School Mode Analytics / Tutorials

• SQL Tutorials SQLZOO / Tutorials

• Data Mining

• Mining Massive Data Sets / Stanford Coursera & Digital & Book $58

• Mining The Social Web Book $30

• Introduction to Information Retrieval / Stanford Digital & Book $56

OSDSM Specialization: Web Scraping & Crawling

• Machine Learning

Foundational & Theoretical

• Machine Learning Ng Stanford / Coursera
• A Course in Machine Learning UMD / Digital Book
• The Elements of Statistical Learning / Stanford Digital & Book $80 & Study Group
• Machine Learning Caltech / Edx

Practical

• Programming Collective Intelligence Book $27

• Machine Learning for Hackers ipynb / digital book

• Intro to scikit-learn, SciPy2013 youtube tutorials

• Probabilistic Modeling

• Probabilistic Programming and Bayesian Methods for Hackers Github / Tutorials

• Probabilistic Graphical Models Stanford / Coursera

• Deep Learning (Neural Networks)

• Neural Networks Andrej Karpathy / Python Walkthrough

• Neural Networks U Toronto / Coursera

• Social Network & Graph Analysis

• Social and Economic Networks: Models and Analysis / Stanford / Coursera

• Social Network Analysis for Startups Book $22

• Natural Language Processing

• From Languages to Information / Stanford CS147 Materials

• NLP with Python (NLTK library) Digital, Book $36

• Analysis

• Python for Data Analysis Book $24

• Big Data Analysis with Twitter UC Berkeley / Lectures

• Exploratory Data Analysis Tukey / Book $81

• Visualization

Foundational Information Design Books

• Envisioning Information Tufte / Book $36
• The Visual Display of Quantitative Information Tufte / Book $27

Theoretical Courses / Design & Visualization

• Data Visualization University of Washington / Slides & Resources
• Berkeley's Viz Class UC Berkeley / Course Docs
• Rice University's Data Viz class Rice University / Slides

Practical Visualization Resources

• D3 Library / Scott Murray Blog / Tutorials
• Interactive Data Visualization for the Web / Scott Murray Online Book & Book $26

OSDSM Specialization: Data Journalism

• 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

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

Command Line Install Script for Scientific Python Packages

• Pandas Cookbook (data structure library)

More Libraries can be found in related specializations

• 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 modeling, 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

  • matplotlib - well-integrated with analysis and data manipulation packages like numpy and pandas
  • 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)

• A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis

• Doing Data Science: Straight Talk from the Frontline O'Reilly / Book $25

• Capstone Analysis of Your Own Design; Quora's Idea Compendium
• Healthcare Twitter Analysis Coursolve & UW Data Science
• Analyze your LinkedIn Network Generate & Download Adjacency Matrix

• DataTau - The "Hacker News" of Data Science
• Metacademy - Search for a concept you want to learn
• Coursera - Online university courses
• Wolfram Alpha - The smart number and info cruncher
• Khan Academy - High quality, free learning videos
• Wikipedia - The free encyclopedia
• 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
• Josh Wills - The Life of a Data Scientist / Video
• Data Scientist Interviews Metamarkets
• /r/MachineLearning Reddit

Non-Open-Source books, courses, and resources are noted with $.

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

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