Just Enough Math
This is a companion wiki + code repository for the O'Reilly Media video Just Enough Math which provides additional links, sample code, and other addenda.
- preview trailer on YouTube
- Docker container for IPython notebooks
- webcast: Computational Thinking: Just Enough Math
- newsletter sign-up
- JEM web site (this site)
- LinkedIn group
- Bay Area meetup group
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 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.
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
- Explore the math examples through typical business use cases
- Understand how these concepts tie into common business frameworks
- Follow a case study of the Foobartendr.io company throughout the course
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:
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 from Continuum Analytics.
We have prepared a GitHub gist to provide the input data and expected results for many of the code examples:
We recommend two excellent resources for learning to program in Python:
- Think Python; Allen Downey; O'Reilly Media (2012)
- Introduction to Python; Jessica McKellar; O'Reilly Media (2014)
Some external sites that also get referenced in the examples include:
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; Nathan Marz, James Warren; Manning (2014)
- Building Data Science Teams; DJ Patil; O’Reilly Media (2011)
Analyzing the Analyzers; Harlan Harris, Sean Murphy, Marck Vaisman; O’Reilly Media (2013)
Introduction to Linear Algebra; Gilbert Strang; Wellesley-Cambridge (2009)
- Elementary Linear Algebra; Keith Matthews; Number Theory Web (1991)
Algebraic Graph Theory; Norman Biggs; Cambridge (1974)
Convex Optimization; Stephen Boyd, Lieven Vandenberghe; Cambridge (2004)
- Linear Programming and Extensions; George Dantzig; Princeton (1963)
- Hidden Order; John Holland; Helix (1996)
A Field Guide to Genetic Programming; Riccardo Poli, William Langdon, Nicholas McPhee; Lulu (2008)
Data Science for Business; Foster Provost, Tom Fawcett; O’Reilly Media (2013)
- Doing Data Science: Straight Talk from the Frontline; Cathy O'Neil, Rachel Schutt; O’Reilly Media (2013)
- Python for Data Analysis; Wes McKinney; O’Reilly Media (2012)