Update README.md

Author: clarecorthell

README.md

@@ -1,24 +1,28 @@
-###### NB: Homepage [here](http://datasciencemasters.org), Community version [HERE](https://github.com/datasciencemasters/go/wiki)
+###### NB: Homepage [here](http://datasciencemasters.org), Community version [HERE](https://github.com/datasciencemasters/go/)
 
-***
-
-The Curriculum for learning Data Science, Open Source and at your fingertips.
+## The Open-Source Curriculum for learning Data Science
 
-## The Internet is Your Oyster
+### The Internet is Your Oyster
 
 I didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?
 
+*See [My Curriculum](http://bit.ly/corthelldata)*
+
 With Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?
 
-## The Motivation
+### 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](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013
 
-## An Academic Shortfall
+> 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](http://blog.bright.com/2012/11/13/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.
 
@@ -30,12 +34,89 @@ Classic academic conduits aren't providing Data Scientists -- this talent gap wi
 
 -- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013
 
-## The Open Source Curriculum
+### Ready?
 
-**[Start Here](http://datasciencemasters.org)**.
+***
+
+## The Open Source Data Science Curriculum
+
+Start here.
+* **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.
+
+### 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#)
+* Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)
+* 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)
+* Coding the Matrix: Linear Algebra through Computer Science Applications [Brown / Coursera](https://www.coursera.org/course/matrix)
+* Think Bayes [Allen Downey / Book](http://www.greenteapress.com/thinkbayes/)
+
+### 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)
+
+* **Databases**
+ * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)
+
+* **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)
+
+* **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](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)
+
+* **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)
+
+* **Natural Language Processing**
+ * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)
+
+* **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)
+
+* **Python** (Learning)
+ * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](http://code.google.com/edu/languages/google-python-class/)
+
+* **Python** (Libraries)
+ * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)
+ * [Data Science in iPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)
+ * 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/)
+
+### Capstone Project
+* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)
+* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium
+
+***
+### Further Study Resources:
+* [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
+* Great 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
+* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)
+* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)
+* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)
 
 ## Contribute
 
-Please Share and Contribute -- **it's Open Source**!
+Please Share and Contribute Your Ideas -- **it's Open Source!**
+
+Here's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki/_pages)!
 
-Follow me on Twitter [@clarecorthell](http://twitter.com/clarecorthell)
+[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)