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icon_preview__cbnn6wmst3de_large created & maintained by skully p,partner of Luminant Data Science Consulting an Update & status for OSS opens 22' <--wp|Silly things in Montgomery|wp-->

The Open-Source Data Science Masters

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.

Contents

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Shortcuts

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?

The Open Source Data Science Curriculum

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Your Home

Intro to Data Science @APPLESTORESONLINEicon_appstore__ey0in3lso2ye_large / UW Videos

Data Science / Harvard Videos & Course

Data Science with Open Source Tools Book $27

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. R resources can be found here.

Ethics in Machine Intelligence

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.

Math

Linear Algebra & Programming

Convex Optimization

Statistics

Differential Equations & Calculus

Computing

Get your environment up and running with the Data Science Toolbox

Algorithms

Distributed Computing Paradigms

Databases

Data Mining

Data Design

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.

OSDSM Specialization: Web Scraping & Crawling

Machine Learning

Foundational & Theoretical

Probabilistic Modeling

Deep Learning (Neural Networks)

Social Network & Graph Analysis

Natural Language Processing

Data Analysis

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.

in Python

Data Communication and Design

Visualization

Data Visualization and Communication

OSDSM Specialization: Data Journalism

Python (Learning)

Python (Libraries)

Installing Basic Packages Python, virtualenv, NumPy, SciPy, matplotlib and IPython & Using Python Scientifically

Command Line Install Script for Scientific Python Packages

More Libraries can be found in the "awesome machine learning" repo & in related specializations

Data Structures & Analysis Packages

Machine Learning Packages

Networks Packages

Statistical Packages

Natural Language Processing & Understanding

Data APIs

Visualization Packages

iPython Data Science Notebooks

Datasets are now here

R resources are now here

Data Science as a Profession

Capstone Project

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Reminders

Read

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Podcast

Learn

Notation

Non-Open-Source books, courses, and resources are noted with $.

Contribute

Please Contribute -- this is Open Source!

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