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| ### The Open-Source Masters | ||
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| I couldn't wait to go back to grad school. Literally. So I designed my own grad school and spent 5 months learning & hacking in great delight! | ||
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| ### My Background ([linkedin](http://bit.ly/clarecorthell)) | ||
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| I'm a Stanford-educated Engineer, previously a Front-End Developer and UX Designer on early-stage products. I'm always in hot pursuit of deeper insight to social questions! | ||
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| ### Goals & Motivations of the Open Source M.S. | ||
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| Data Science is an ideal marriage for my technical capacities, social research inquisitions, and my geekish-freakish love of statistics. | ||
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| ### Next Steps? | ||
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| I'm now a Data Scientist with an incredible team at [Mattermark](http://www.mattermark.com)! | ||
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| *** | ||
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| ## The Data Science Curriculum / April-August 2013 | ||
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| * **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci) | ||
| * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization. | ||
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| ### Math | ||
| * Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#) | ||
| * Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do) | ||
| * Problem-Solving Heuristics "How To Solve It" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It) | ||
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| ### Computing | ||
| * **Algorithms** | ||
| * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo) | ||
| * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms) | ||
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| * **Databases** | ||
| * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db) | ||
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| * **Data Mining** | ||
| * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html) | ||
| * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do) | ||
| * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html) | ||
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| * **Machine Learning** | ||
| * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml) | ||
| * Programming Collective Intelligence [O'Reilly / Book](http://shop.oreilly.com/product/9780596529321.do) | ||
| * Statistics [The Elements of Statistical Learning / Book](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) ** *en process* | ||
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| * **Probabilistic Graphical Models** | ||
| * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials] (https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) | ||
| * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm) ** *en process* | ||
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| * **Natural Language Processing** | ||
| * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do) | ||
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| * **Analysis** | ||
| * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784) | ||
| * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/) | ||
| * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline) | ||
| * Information Visualization ["Envisioning Information" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design) | ||
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| * **Python** (Learning) | ||
| * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](code.google.com/edu/languages/google-python-class/) | ||
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| * **Python** (Libraries) | ||
| * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/) | ||
| * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc) | ||
| * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis) | ||
| * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython) | ||
| * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/) | ||
| * Network Modeling & Viz [networkx](http://networkx.github.io/) | ||
| * Natural Language Toolkit [NLTK](http://nltk.org/) | ||
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| ### Projects | ||
| * Coursework | ||
| * Sentiment analysis, trending topics, and friendship mapping with Twitter API | ||
| * Joins and Matrix Manipulation in MapReduce (AWS EC2) | ||
| * In-database Text analysis (SQL) | ||
| * Sentiment analysis of movie tweets (Python) | ||
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| *** | ||
| ### A Note on Tools | ||
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| This degree is brought to you by: "THE INTERNET". | ||
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| Information is more democratized^ now than it was at any point in history. Given a little initiative and interest, you can tailor and excel in an education of your own design. The connective web made me what I am today, growing from the child obsessed with [Number Munchers](http://en.wikipedia.org/wiki/Munchers#Number_Munchers) to an adult jaw-dropping over [DBSCAN](http://en.wikipedia.org/wiki/DBSCAN). | ||
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| The most valuable resources I used were: | ||
| * [Coursera](http://coursera.org) | ||
| * [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability) | ||
| * [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus) | ||
| * [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases) | ||
| * [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science) | ||
| * **Kindle .mobis** (carrying textbooks is so 90s.) | ||
| * PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver | ||
| * **Friends & Family** (Impossible without their support! Special Thanks to N.S.) | ||
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| *^ given internet access - an issue near and dear to me.* | ||
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| *** | ||
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| ### I "Forked" this into the [Open Source Data Science Masters](http://datasciencemasters.org) Curriculum. | ||
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| [Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell) |