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The Open-Source Data Science Masters - Curriculum

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?

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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.

Math

• Linear Algebra & Programming

• Linear Algebra / Levandosky Stanford / Book

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

• Coding the Matrix: Linear Algebra through Computer Science Applications Brown / Coursera

• Statistics

• Statistics Stats in a Nutshell / Book

• Think Stats: Probability and Statistics for Programmers Allen Downey / Book

• Think Bayes Allen Downey / Book

• An Introduction to Statistical Learning Book pdf ^also a Machine Learning resource *uses R

• Forecasting: Principles and Practice (Regression, Time Series, Forecasting) Monash University / Book *uses R

• Problem Solving

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

Computing

• Algorithms

• Algorithms Design & Analysis I Stanford / Coursera

• Algorithm Design Kleinberg & Tardos / Book

• Distributed Computing Paradigms

• NB: The UW Intro to Data Science does a great job teaching hadoop UW / Coursera

• Intro to Hadoop and MapReduce

• Databases

• SQL Tutorial W3Schools / Tutorials

• Another SQL Tutorial SQLZOO

• Introduction to Databases Stanford / Online Course

• Data Mining

• Mining Massive Data Sets Stanford / Book

• Mining The Social Web O'Reilly / Book and Blog

• Introduction to Information Retrieval Stanford / Book

• Machine Learning

• Machine Learning Ng Stanford / Coursera

• A Course in Machine Learning UMD /Online Book

• Programming Collective Intelligence O'Reilly / Book

• The Elements of Statistical Learning Stanford / Book^

• Statistical Learning Stanford / OpenEdX

• Machine Learning Caltech / Edx

• Neural Networks U Toronto / Coursera

• Probabilistic Graphical Models

• Probabilistic Programming and Bayesian Methods for Hackers Github / Tutorials

• PGMs / Koller Stanford / Coursera

• Natural Language Processing

• NLP with Python / NLTK O'Reilly / Book, Free online version

• Analysis

• Python for Data Analysis O'Reilly / Book

• Big Data Analysis with Twitter UC Berkeley / Lectures

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

• Visualization

• Information Visualization "Envisioning Information" Tufte / Book

• Data Visualization Harvard CS 171 / Lectures

• Scott Murray's Tutorial on D3 Scott Murray's Blog / Tutorials

• Berkely's Viz Class UC Berkeley / Course

• Rice University's Data Viz class [Rice University] (http://had.co.nz/stat645/)

• Python (Learning)

• Learn Python the Hard Way eBook

• Python Class / Google

• Think Python

• Introduction to Computer Science and Programming MIT OpenCourseWare / Lectures

• Python (Libraries)

• Basic Packages Python, virtualenv, NumPy, SciPy, matplotlib and IPython

• Data Science in iPython Notebooks (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)

• Bayesian Inference | pymc

• Labeled data structures objects, statistical functions, etc pandas (See: Python for Data Analysis)

• Python wrapper for the Twitter API twython

• Tools for Data Mining & Analysis scikit-learn

• Network Modeling & Viz networkx

• Natural Language Toolkit NLTK

• R (Learning)

• Software Design: The Art of R Programming O'Reilly / Book

• R in a Nutshell O'Reilly / Book

• Viz and Elegant Graphics in R: ggplot2 Springer / Book

• Machine Learning in R Tutorial

• Guide to Getting Started in Machine Learning Tutorial

• Learn R & Become a Data Analyst Tutorial

• Introduction to Data Science Syracuse University / ebook

• R (Libraries)

• Natural Language Toolkit OpenNLP

• Text Mining [tm](http://cran.r-project.org/web/packages/tm/index.html]

• Basic Viz wordcloud

• Network Modeling & Viz igraph

• Basic Machine Learning e1071

• Kernel Method kernlab

• Chinese Language Processing Rwordseg

• Chinese Weibo Analysis Rweibo

Capstone Project

• Toy Data Ideas
• Capstone Analysis of Your Own Design; Quora's Idea Compendium
• Healthcare Twitter Analysis Coursolve & UW Data Science

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Further Study Resources:

• Coursera
• Khan Academy
• Metacademy
• Wolfram Alpha
• Wikipedia
• Great PopSci Read: The Signal and The Noise Nate Silver
• Zipfian Academy's List of Resources
• A Software Engineer's Guide to Getting Started with Data Science
• Data Scientist Interviews Metamarkets
• /r/MachineLearning Reddit

Sources for Datasets:

• NIPS Feature Selection
• Stanford Network Analysis Project
• Data Science Contests [Kaggle] (https://www.kaggle.com/)
• @hmason's curated dataset list bit.ly
• Classical Datasets for Your Specific Need UCI Machine Learning Repository Datasets
• Time Series Data Library
• USA Congressional Voting Records Voteview
• Qandl provides a lot of interesting data with a clean API.

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, so I've explicitly marked when resources use other tools to teach conceptual material (like R)

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