The Open Source Data Science Masters
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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 making use of data.
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
Intro to Data Science / UW Videos * 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 Videos & 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 Tools Book
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.
Human impact is a first-class concern when building machine intelligence technology. When we build products, we deduce patterns and then reinforce them in the world. Ethics in any Engineering concerns understanding the sociotechnological impact of the products and services we are bringing to bear in the human world -- and whether they are reinforcing a future we all want to live in. * Index: Cultural Bias in Machine Intelligence
Get your environment up and running with the Data Science Toolbox
How does the real world get translated into data? How should one structure that data to make it understandable and usable? Extends beyond database design to usability of schemas and models. * Tidy Data in Python
OSDSM Specialization: Web Scraping & Crawling
Foundational & Theoretical * Machine Learning Ng Stanford / Coursera & Stanford CS 229 * A Course in Machine Learning UMD / Digital Book * The Elements of Statistical Learning / Stanford Digital & BookStudy Group * Machine Learning Caltech / Edx
Practical * Programming Collective Intelligence Bookipynb / digital book * Intro to scikit-learn, SciPy2013 youtube tutorials
One of the "unteachable" skills of data science is an intuition for analysis. What constitutes valuable, achievable, and well-designed analysis is extremely dependent on context and ends at hand.
Data Visualization and Communication * The Truthful Art: Data, Charts, and Maps for Communication Cairo / Book
Theoretical Design of Information
Applied Design of Information * Information Dashboard Design: Displaying Data for At-a-Glance Monitoring Stephen Few / Book
Theoretical Courses / Design & Visualization
Practical Visualization Resources
OSDSM Specialization: Data Journalism
Installing Basic Packages Python, virtualenv, NumPy, SciPy, matplotlib and IPython & Using Python Scientifically
Command Line Install Script for Scientific Python Packages
$15- Bestseller Pop Sci
Non-Open-Source books, courses, and resources are noted with
Please Contribute -- this is Open Source!