The Open Source Data Science Masters
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transcripts
LICENSE.md
README.md
analysis-technologies.md
basic-programming.md
blogs-n-media.md
database-tech.md
datasets.md
machine-learning.md
r-resources.md
specializations.md

README.md

Follow me on Twitter @clarecorthell

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 make data useful.

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.

...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

Start here. Intro to Data Science UW / Coursera

  • 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 Video Archive & 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 $27

  • 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.

Math

[★ What are some good resources for learning about numerical analysis? / Quora ] (http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)

Computing

OSDSM Specialization: Web Scraping & Crawling

OSDSM Specialization: Data Journalism

Python (Learning)

Python (Libraries)

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

    • 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 modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
  • Live Data Packages

    • twython - Python wrapper for the Twitter API
  • Visualization Packages

    • Orange - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining
  • iPython Data Science Notebooks

  • Data Science in IPython Notebooks (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)

  • A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis

Datasets are now here

R resources are now here

Data Science as a Profession

Capstone Project


Resources


Notation

Paid books, courses, and resources are noted with $.

Contribute

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

Follow me on Twitter @clarecorthell