I design tools that promote animal health and welfare in the zoo and agricultural industries.

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

- Same as first text, but more Baysian!

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

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

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

a coursera course that several folks online recommended.

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

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.