Need help with go?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

19.5K Stars 5.9K Forks The Unlicense 296 Commits 84 Opened issues


The Open Source Data Science Masters

Services available


Need anything else?

Contributors list

created & maintained by @clarecorthell, founding partner of Luminant Data Science Consulting

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.


The Internet is Your Oyster

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. 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


The Open Source Data Science Curriculum

Start here.

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

* Topics: Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics * Example Code in: R, Python, Sage, C, Gnu Scientific Library

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. * Index: Cultural Bias in Machine Intelligence


Linear Algebra & Programming

Convex Optimization


Differential Equations & Calculus


Get your environment up and running with the Data Science Toolbox


Distributed Computing Paradigms


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. * Tidy Data in Python

OSDSM Specialization: Web Scraping & Crawling

Machine Learning

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 & Book

& Study Group * Machine Learning Caltech / Edx

Practical * Programming Collective Intelligence Book

* Machine Learning for Hackers ipynb / digital book * Intro to scikit-learn, SciPy2013 youtube tutorials

Probabilistic Modeling

Deep Learning (Neural Networks)

Social Network & Graph Analysis

Natural Language Processing

  • From Languages to Information / Stanford CS147 Materials
  • NLP with Python (NLTK library) Digital, Book
  • How to Write a Spelling Correcter / Norvig (Tutorial)[]

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 Analysis in Python Tutorial
  • Python for Data Analysis Book
  • An Example Data Science Process ipynb

Data Communication and Design


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

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

  • PyMC - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit
  • Statsmodels - Python module that allows users to explore data, estimate statistical models, and perform statistical tests
  • PyMVPA - Multivariate Pattern Analysis in Python

Natural Language Processing & Understanding

  • NLTK - Natural Language Toolkit
  • Gensim - Python library for topic modeling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

Data APIs

  • twython - Python wrapper for the Twitter API

Visualization Packages

  • matplotlib - well-integrated with analysis and data manipulation packages like numpy and pandas
  • Seaborn - a high-level statistical visualization package built on top of matplotlib

iPython Data Science Notebooks

Datasets are now here

R resources are now here

Data Science as a Profession

  • Doing Data Science: Straight Talk from the Frontline O'Reilly / Book
  • The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists Book

Capstone Project



Watch & Listen



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



Please Contribute -- this is Open Source!

Follow me on Twitter @clarecorthell

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.