Curriculum for Data Science
This project is maintained by datasciencemasters
Follow me on Twitter @clarecorthell
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 Science UW / Coursera
Data Science / Harvard Video Archive & Course
Data Science with Open Source Tools Book $27
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
Linear Algebra & Programming
$10
Statistics
Differential Equations & Calculus
Problem Solving
$10
Algorithms
$125
Distributed Computing Paradigms
$29
Databases
Data Mining
$58
$30
$56
OSDSM Specialization: Web Scraping & Crawling
Machine Learning
$27
$80
Statistical Network Analysis & Modeling
Network & Graph Analysis
$22
Natural Language Processing
Analysis
$24
$81
Visualization
$36
$27
OSDSM Specialization: Data Journalism
$23
$34
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
Machine Learning Packages
Networks Packages
Statistical Packages
Natural Language Processing & Understanding
Live Data Packages
Visualization Packages
$25
$15
Paid 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