The Open-Source Data Science Masters

This is my forked version of the [Data Science Masters] (http://datasciencemasters.org/) open-source curriculum logging my journey.

Background

Personal Website [LinkedIn] (ca.linkedin.com/in/andrewandrade/) Github Hackster.io

I am a University of Waterloo engineering undergraduate studying Mechatronics Engineering. My vision is to design, develop and depoy products which revolutionize the world. My mission is to close the gap between idea execution and success, so my focus is on building robust production level systems.

My prior experience is broad in scope and narrow in focus, constantly focusing on the most impact for minimal input. My previous internships and employement has been in Mechanical, Electrical and Robotic Hardware Design and Manufacturing, Embedded Software Development, Petroleum Engineering (with a focus on reservoir and production engineering), and data science in addition to some soft leadership roles. I am a co-founder of [PetroPredict] (petropredict.com) currently leading both the technology development and research conducted at the company.

My current focus and research and development interests are in quantative engineering in electromechnical integration related to machine learning, probalistic robotics, man-machine symbiosis, and knowledge discovery in databases.

The Open Source Data Science Curriculum

###Prerequisites: I am a strong believer in building production level systems and also in efficiency and using the right tool for the job. The prerequisites list here are not mandatory for data science, but I think provide both a good background and toolset to succeed as a data scientist.

Version Control

• Using version control is definatly a must, and GIT has become a daily habit
• If you are completly new, the Udacity course on How to Use GIT and GitHub is a great start
• [Git Immersion] (http://gitimmersion.com/) is also a great guide to get started
• [Pro Git] (http://gitimmersion.com/) is a very indepth guide to mostly everything you would need to learn git.
• [More tutorials] (http://sixrevisions.com/resources/git-tutorials-beginners/) if you are still having trouble
• [StackOverflow Examples] (http://stackoverflow.com/questions/315911/git-for-beginners-the-definitive-practical-guide)

Backg New to data science with no experience in coding? Start with: Datasmart Book

• Topics: The basics of data science in a nontrivial way, basic machine learning algorithms with implementation in Excel and R

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)

• Linear Algebra & Programming

• Linear Algebra / Levandosky Stanford / Book $10

• Linear Programming (Math 407) University of Washington / Course

• Statistics

• Statistics I Princeton / Coursera

• Stats in a Nutshell Book $29

• Think Stats: Probability and Statistics for Programmers Digital & Book $25

• Think Bayes Digital & Book $25

• Differential Equations & Calculus

• Differential Equations in Data Science Python Tutorial

• Problem Solving

• Problem-Solving Heuristics "How To Solve It" Polya / Book $10

Computing

• Algorithms

• Algorithms Design & Analysis I Stanford / Coursera

• Algorithm Design, Kleinberg & Tardos Book $125

• Distributed Computing Paradigms

• *See Intro to Data Science UW / Lectures on MapReduce

• Intro to Hadoop and MapReduce Cloudera / Udacity Course *includes select free excerpts of Hadoop: The Definitive Guide Book $29

• Databases

• Introduction to Databases Stanford / Online Course

• SQL School Mode Analytics / Tutorials

• SQL Tutorials SQLZOO / Tutorials

• Data Mining

• Mining Massive Data Sets Stanford / Digital & Book $58

• Mining The Social Web Book $30

• Introduction to Information Retrieval / Stanford Digital & Book $56

OSDSM Specialization: Web Scraping & Crawling

• Machine Learning

Foundational & Theoretical

• Machine Learning Ng Stanford / Coursera
• A Course in Machine Learning UMD / Digital Book
• The Elements of Statistical Learning / Stanford Digital & Book $80
• Machine Learning Caltech / Edx

Practical

• Programming Collective Intelligence Book $27

• Machine Learning for Hackers ipynb / digital book

• Intro to scikit-learn, SciPy2013 youtube tutorials

• Statistical Network Analysis & Modeling

• Probabilistic Programming and Bayesian Methods for Hackers Github / Tutorials

• Probabalistic Graphical Models Stanford / Coursera

• Neural Networks U Toronto / Coursera

• Network & Graph Analysis

• Social and Economic Networks: Models and Analysis / Stanford / Coursera

• Social Network Analysis for Startups Book $22

• Natural Language Processing

• NLP with Python (NLTK library) Digital, Book $36

• Analysis

• Python for Data Analysis Paper Book $24

• Big Data Analysis with Twitter UC Berkeley / Lectures

• Exploratory Data Analysis Tukey / Book $81

Data Design

• Visualization

Foundational Information Design Books

• Envisioning Information Tufte / Book $36
• The Visual Display of Quantitative Information Tufte / Book $27

Theoretical Courses / Design & Visualization

• Data Visualization University of Washington / Slides & Resources
• Berkeley's Viz Class UC Berkeley / Course Docs
• Rice University's Data Viz class Rice University / Slides

Practical Visualization Resources

• D3 Library / Scott Murray Blog / Tutorials
• Interactive Data Visualization for the Web / Scott Murray Online Book & Book $26

OSDSM Specialization: Data Journalism

Python (Learning)

• Learn Python the Hard Way Digital & Book $23
• Python Class / Google
• Think Python Digital & Book $34
• Introduction to Computer Science and Programming MIT OpenCourseWare / Lectures

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

  • Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc pandas & Tutorials Python for Data Analysis / Book

• Machine Learning Packages

  • scikit-learn - Tools for Data Mining & Analysis

• Networks Packages

  • networkx - Network Modeling & Viz

• 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

  • matplotlib - well-integrated with analysis and data manipulation packages like numpy and pandas
  • 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

• Doing Data Science: Straight Talk from the Frontline O'Reilly / Book $25

Capstone Project

• Capstone Analysis of Your Own Design; Quora's Idea Compendium
• Healthcare Twitter Analysis Coursolve & UW Data Science

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Resources

• DataTau - The "Hacker News" of Data Science
• Metacademy - Search for a concept you want to learn
• Coursera - Online university courses
• Wolfram Alpha - The smart number and info cruncher
• Khan Academy - High quality, free learning videos
• Wikipedia - The free encyclopedia
• The Signal and The Noise - Nate Silver Pop-Sci Data Analysis $15
• Zipfian Academy's List of Resources
• A Software Engineer's Guide to Getting Started with Data Science
• Data Scientist Interviews Metamarkets
• /r/MachineLearning Reddit

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Notation

Non-Open-Source 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