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ceteri committed Jul 11, 2014
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@@ -4,12 +4,20 @@ This is a companion wiki + code repository for the O'Reilly Media video [Just En
* [preview trailer](http://youtu.be/TQ58cWgdCpA) on YouTube
* [webcast](http://www.oreilly.com/pub/e/3114): *Computational Thinking: Just Enough Math*
* [mailing list](http://liber118.com/pxn/)
+ * [web site](http://justenoughmath.com/)
---
-With the commercial successes of machine learning and cloud computing, many business people need just enough math to take advantage of open source frameworks for big data. This video course from Paco Nathan and Allen Day presents useful areas of advanced math in easy-to-digest morsels. If you’re familiar with high school Algebra 2 and basic statistics, you’re good to go.
+With the commercial successes of machine learning and cloud computing,
+many business people need just enough math to take advantage of open source frameworks for big data.
+This course from [Paco Nathan](https://twitter.com/pacoid) and [Allen Day](https://twitter.com/allenday)
+presents useful areas of advanced math in easy-to-digest morsels.
+If you’re familiar with high school Algebra 2 and basic statistics, you’re good to go.
-You’ll learn newly introduced math concepts through business use cases, brief Python code examples, and lots of figures and illustrations. By the end of the course, you’ll understand how to leverage complex graphs, sparse matrices, Bayesian priors, optimization solvers, and other tools.
+You’ll learn newly introduced math concepts through business use cases, brief Python code examples,
+and lots of figures and illustrations.
+By the end of the course, you’ll understand how a variety of advanced math techniques get leveraged:
+complex graphs, sparse matrices, Bayesian priors, optimization solvers, etc.
* Learn advanced math through simple equations and illustrations
* Get tangible examples such as Lego blocks for data workflows
@@ -18,15 +26,18 @@ You’ll learn newly introduced math concepts through business use cases, brief
* Follow a case study of the *Foobartendr.io* company throughout the course
### Computational Thinking
-Throughout this material, we use an instructional rubric called *computational thinking* to develop themes for how to approach for Big Data. Some great resources for CT include:
+Throughout this material, we use an instructional rubric called *computational thinking* to develop themes for how to approach for Big Data.
+Some great resources for CT include:
* [Center for Computational Thinking](http://www.cs.cmu.edu/~CompThink/) @ CMU
* [Exploring Computational Thinking](https://www.google.com/edu/computational-thinking/) @ Google
### Programming Environment
-Most all of the programming examples are based on small sections of Python code. If you have Python installed, then launch its command line prompt and proceed to cut and paste the example code.
+Most all of the programming examples are based on small sections of Python code.
+If you have Python installed, then launch its command line prompt and proceed to cut and paste the example code.
-To get Python installed on your laptop, we recommend using the free download of [Anaconda](https://store.continuum.io/cshop/anaconda/) from Continuum Analytics.
+To get Python installed on your laptop, we recommend using the free download of
+[Anaconda](https://store.continuum.io/cshop/anaconda/) from Continuum Analytics.
We have prepared a GitHub *gist* to provide the input data and expected results for many of the code examples:
@@ -45,21 +56,25 @@ Some external sites that also get referenced in the examples include:
---
### Other Resources
-Part of the intended purpose for this video+book+tutorial series of material is to provide many links to other resources. The interested reader has more than enough "jumping" points to explore particular topics in depth:
+Part of the intended purpose for this video+book+tutorial series of material is to provide many links to other resources.
+The interested reader has plenty of "jumping" points from which to explore particular topics in depth:
* [Big Data](http://manning.com/marz/); Nathan Marz, James Warren; Manning (2014)
* [Building Data Science Teams](http://oreilly.com/data/free/building-data-science-teams.csp); DJ Patil; O’Reilly Media (2011)
- * [Analyzing the Analyzers](http://oreilly.com/data/free/analyzing-the-analyzers.csp); Harlan Harris, Sean Murphy, Marck Vaisman; O’Reilly Media (2013)
-
+ * [Analyzing the Analyzers](http://oreilly.com/data/free/analyzing-the-analyzers.csp); Harlan Harris, Sean Murphy, Marck Vaisman; O’Reilly Media (2013)
+
+
* [Introduction to Linear Algebra](http://math.mit.edu/linearalgebra); Gilbert Strang; Wellesley-Cambridge (2009)
* [Elementary Linear Algebra](http://numbertheory.org/book/); Keith Matthews; Number Theory Web (1991)
* [Algebraic Graph Theory](http://amazon.com/dp/0521458978); Norman Biggs; Cambridge (1974)
+
* [Convex Optimization](http://amazon.com/dp/0521833787); Stephen Boyd, Lieven Vandenberghe; Cambridge (2004)
* [Linear Programming and Extensions](http://amazon.com/dp/0691059136); George Dantzig; Princeton (1963)
* [Hidden Order](http://amazon.com/dp/0201442302); John Holland; Helix (1996)
* [A Field Guide to Genetic Programming](http://amazon.com/dp/1409200736); Riccardo Poli, William Langdon, Nicholas McPhee; Lulu (2008)
+
* [Data Science for Business](http://shop.oreilly.com/product/0636920028918.do); Foster Provost, Tom Fawcett; O’Reilly Media (2013)
* [Doing Data Science: Straight Talk from the Frontline](http://shop.oreilly.com/product/0636920028529.do); Cathy O'Neil, Rachel Schutt; O’Reilly Media (2013)
* [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do); Wes McKinney; O’Reilly Media (2012)
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