Rundel Open Intro Stats 3rd ed..

• Probability
• Distribution of random numbers
• Foundations for inference
• Inference for numerical data
• Inference for categorical data
• Introduction to linear regression
• Multiple and logistic regression

Rundel Open Intro Stat with Randomization and Simulation 1st ed.

• Same as first text, but more Baysian!

An Introduction to Statistical Learning, 7th ed.

• Statistical Learning
• Linear Regression
• Classification
• Resampling Methods
• Linear Model Selection and Regularization
• Moving Beyond Linearity
• Tree Based Methods
• Support Vector Machines
• Unsupervised Learning

Think Bayes

And the related blog, Probably Overthinking It.

The examples are in Python, so I will need to wait until I have learned a bit of Python to tackle this one.

• Baye’s theorem
• Computational Stats
• Estimation
• More estimation
• Odds and addends
• Decision analysis
• Prediction bias
• Observer bias
• Two dimensions
• Approximate Bayesian computation
• Evidence
• Simulation
• Hierarchical models
• Dimensions

Probabilistic Programming and Bayesian Methods for Hackers

Another Python-centric one that will come later.

• Introduction to Bayesian Methods
• A little more on PyMC
• Opening the Black Box of MCMC
• The Greatest Theorem Never Told
• Would you rather lose an arm or a leg?
• Getting our prior-ities straight
• Bayesian methods in Machine Learning and Model Validation
• More PyMC Hackery

Designing, Running, and Analyzing Experiments

a coursera course that several folks online recommended.

DataCamp Linear Algebra Course

I want to learn more, but probably don’t need a rigorous background for my purposes. This will probably do me.

Some sort of A/B testing thing

I feel like quite a bit of the jobs require UI/UX interface testing and analysis. Haven’t picked out a tool, but probably fits here.