The Curriculum for learning Data Science, Open Source and at your fingertips.
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
Probabilistic Graphical Models
Natural Language Processing
This is an introduction geared toward those with at least a minimal 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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