NB: Fancy Homepage here (You are currently seeing the living document)
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, stackoverflow, 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 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.
- Linear Algebra / Levandosky Stanford / Book
- Linear Programming (Math 407) University of Washington / Course
- Statistics Stats in a Nutshell / Book
- Forecasting: Principles and Practice Monash University / Book *uses R
- Problem-Solving Heuristics "How To Solve It" Polya / Book
- Coding the Matrix: Linear Algebra through Computer Science Applications Brown / Coursera
- Think Bayes Allen Downey / Book
- Think Stats: Probability and Statistics for Programmers Allen Downey / Book
-
Algorithms
-
Algorithms Design & Analysis I Stanford / Coursera
-
Algorithm Design Kleinberg & Tardos / Book
-
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
-
Introduction to Information Retrieval Stanford / Book
-
Machine Learning
-
Machine Learning / Ng Stanford / Coursera
-
A Course in Machine Learning / Hal Daumé III UMD Online Book
-
Programming Collective Intelligence O'Reilly / Book
-
Statistics The Elements of Statistical Learning
-
Machine Learning / CaltechX Caltech / Edx
-
Probabilistic Graphical Models
-
Probabilistic Programming and Bayesian Methods for Hackers Github / Tutorials
-
PGMs / Koller Stanford / Coursera
-
Natural Language Processing
-
NLP with Python O'Reilly / Book
-
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
-
Information Visualization "Envisioning Information" Tufte / Book
-
Python (Learning)
-
New To Python: Learn Python the Hard Way, Google's Python Class
-
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
- Toy Data Ideas
- Capstone Analysis of Your Own Design; Quora's Idea Compendium
- Healthcare Twitter Analysis Coursolve & UW Data Science
- Coursera
- Khan Academy
- Metacademy
- Wolfram Alpha
- Wikipedia
- Quora
- Kindle .mobis
- Great PopSci Read: The Signal and The Noise Nate Silver
- Zipfian Academy's List of Resources
- A Software Engineer's Guide to Getting Started w Data Science
- Data Scientist Interviews Metamarkets
- Harvard's Data Viz Class [Harvard CS 171] (http://cs171.org)
- D3 Tips and Tricks [Leanpub] (https://leanpub.com/D3-Tips-and-Tricks)
- Scott Murray's Tutorial on D3 [Scott Murray's Blog] (http://alignedleft.com/tutorials/)
- Berkely's Viz Class [UC Berkeley] (http://vis.berkeley.edu/courses/cs294-10-sp11/wiki/index.php/CS294-10_Visualization)
- Rice University's Data Viz class [Rice University] (http://had.co.nz/stat645/)
- Interactive Data Visualization Book [O'Reilley] (http://chimera.labs.oreilly.com/books/1230000000345/index.html)
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)
Please Share and Contribute Your Ideas -- it's Open Source!
Here's my transcript; Please showcase your own on the wiki!