Statistics

This course introduces the digital tools and methods used for research in the Humanities.  The theoretical part of the course focuses on basic concepts that are essential for working with large quantities of humanities data, including corpora and databases, searching techniques, information retrieval, and statistical language models. In the practical part of the course, students learn how to do basic text analysis using the programming language Python.
 

This course introduces the fundamental principles behind methods in pharmaceutical modeling and provides hands-on experience with methods used in academia and industry. It focuses on mathematical models and computer programming for a quantitative understanding of diverse pharmaceutically relevant problems. This includes models at different scales, both for molecular and particle level properties, interactions between molecules and particles, and their interactions with the organism. The course uses practical examples to provide the theory behind methods used for pharmaceutical modeling and simulation of system behavior. It begins with a introduction and refresher of fundamental mathematical tools, then applies and modifies computer scripts that model the pharmaceutical systems, and discusses these models in relation to the literature.

This course provides an introduction to scientific research based on statistical methods. It covers basic techniques of probability and statistics for scientific research. The course requires knowledge of calculus (intermediate-level mathematics).

The course discusses probability, distribution theory, and statistical inference. It covers mathematical statistics as important discrete and continuous probability distributions (such as the Binomial, Poisson, Uniform, Exponential, and Normal distributions) and investigates properties of these distributions, including use of the moment generating function. The course discusses point estimation techniques including method of moments, maximum likelihood, and least squares estimation. Statistical hypothesis testing and confidence interval construction follow, along with non-parametric and goodness-of-fit tests and contingency tables. A treatment of linear regression models, featuring the interpretation of computer-generated regression output and implications for prediction are also covered.