MITx: 15.071x The Analytics Edge
MITx: 6.00.1x Introduction to Computer Science and Programming Using Python
HarvardX: PH525.1x Statistics and R for the Life Sciences
HarvardX: PH525.2x Matrix Algebra and Linear Models
HarvardX: PH525.3x Advanced Statistics for the Life Sciences
DavidsonX: D003x.2 Applications of Linear Algebra (Part 2)
Johns Hopkins: The Data Scientist’s Toolbox
Johns Hopkins: R Programming
MOOCs I recommend
Math and Statistics
Stanford: StatLearning Statistical Learning
Duke: Data Analysis and Statistical Inference
The Caltech-JPL Summer School on Big Data Analytics
DavidsonX: D003x.1 Applications of Linear Algebra (Part 1)
Khan Academy: Linear Algebra (140 videos)
My most memorable instance of data-driven decision-making was my development of a simple and profitable strategy for online poker. It also provided content for my guest lectures for Harvey Mudd College’s popular Mathematics of Games course on how the combination of data and mathematics can result in successful strategies that defy conventional wisdom.
It all started when I saw a “poker corner” segment on TV stating that a player who is short-stacked (has few chips remaining) has only one move: all-in. This was presented as a bad situation, but in my mind it was a great opportunity to make the game tractable. Some poker sites allowed you to start with a short-stack, so if my hypothesis was correct, I could actually make a profit. Being somewhat risk-averse, I only ever deposited $50 into my online poker account.
After utilizing an initial all-in or fold strategy that allowed me to gather hand history files on my opponents, I engineered an exploitive strategy by calculating the expected call equity (value when my bet is called), fold equity (value when everyone folds), and the cost of patience (the blinds). Conventional wisdom states that repetitive strategies can’t work, and that your specific opponents and position at the table are the most important things to consider. However, my data was telling me that all of this was incorrect and that a handy profit could be made.
While the strategy was simple, the analysis was not. In addition to creating a predictive model to evaluate potential strategies, I also had to estimate my precise edge in the game, in order to use the Kelly Criterion to minimize exposure to bad luck while maximizing hourly winnings.
In the end, my $50 became $30,000, and after sharing the strategy with friends, we collected some crazy stories to tell disbelieving family members.
UC Berkeley's MIDS Program (Master of Information and Data Science)
(Video-only and bridge courses at UC Berkeley)
MIDS 1a - Fundamentals of Linear Algebra
MIDS 1b - Fundamentals of Data Structures and Algorithms
INFO W18 - Python Bridge