Statistics

This course covers statistical inference in one population, key concepts in hypothesis testing, issues of comparing two populations, concepts of the simple linear regression model, and use of statistical software to perform analyses. Prerequisite: introductory course in statistics.

This course introduces students to the statistical computing and programming, with the main focus on R, Python, and SAS. Students learn basic computing and programming concepts including scripting, variables, expressions, assignments, control structures, and data structures. On the statistical side, they will learn to load raw data, make numerical and graphical summaries of data, and conduct various estimation and testing procedures. Topics include descriptive statistics, statistical estimation, robust estimation, categorical data analysis, testing hypotheses, ANOVA, regression analysis, performing resampling methods and simulations. Some basic knowledge of R is assumed.

This course is part of the LM degree program and so is intended for advanced level students. Enrollment is by consent of the instructor. This course discusses fundamentals of the most important multivariate techniques that help to make intelligent use of large data base by recognizing patterns for predicting or estimating an output based on one or more inputs. At the end of the course the student is able; to represent and organize knowledge about big data collections; to turn data into actionable knowledge; and to choose the best suited methodology for the problem at hand to critically interpret the results. The course discusses topics including an introduction to supervised statistical learning; resampling methods: Cross-Validation, and Bootstrap; classification: Naive Bayes, k-Nearest Neighbors, Logistic Regression, and Linear Discriminant Analysis; Dimension Reduction and Regularization; Tree-based methods: Regression and Classification trees, Bagging, Random Forests, and Boosting; and an overview of the main machine learning methods: Support Vector Machines, and Neural Networks.

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.