The main topics to be covered will be as follows:
Day 1 (5/30- 6pm-9pm): Probability, Probability distributions, Bayesian and frequentist paradigms
Day 2 (6/1 – 6pm-9pm): Random variables, Statistical inference, Regression, Classification, Evaluation metrics
We well begin in the first day with a gentle introduction to probability and the major distributions used in statistics. We will finish with a concept-driven explanation of frequentist and Bayesian statistics.
On the second day we will dive a bit further into how to make use of probability distributions for inference and hypothesis testing. We will then introduce regression and classification through the use of examples. Finally, we will discuss some of the commonly use methods of evaluating model results.
We will use Python to illustrate the concepts, but no working knowledge of Python or any other language is required.