Statistical techniques are essential to most sciences and computer/data science is no exception. However, the statistical techniques that are most fundamental in a given discipline differ from others. For the data scientist, some techniques are more approachable simply because one already knows how to program. In other cases, statistical analysis may be over larger datasets, be more exploratory in nature, or be focused more on prediction.
This interdisciplinary course introduces the mathematical concepts required to fill the gaps between statistics, computer science, and the applications of data science. One should learn to interpret results and subsequently draw conclusions from data in an applied manner. The course presents different techniques for applied statistical inference and data analysis, including their implementation in Python, such as parameter and distribution estimators, hypothesis testing, Bayesian inference, and likelihood.