Statistics

An introduction to probability with a view toward applications. Topics include mathematical models for random phenomena, random variables, expectation, the common discrete and continuous distribution with applications, joint distributions, conditional distributions and expectation, independence, monent generating functions, laws of large numbers and the central limit theorem, sample and population, sample distributions, concept of estimation for population parameters, and linear regression and correlation.

Textbook: Thomas Haslwanter, "AN INTRODUCTION TO STATISTICS WITH PYTHON"

This course introduces "open" tools and methods for processing and visualizing data types such as structured data, text data, and temporal data. It discusses the opportunities and challenges in relation to working with large amounts of data, including ethical conditions regarding data acquisition, storage, aggregation, publication, and use. The course applies theories and concepts to define and analyze issues relating to large amounts of data. Students learn to develop solutions for retrieval and sorting structured and unstructured data, as well as process and represent data visually. The course largely involves hands-on cases working with relevant data sets, including an introduction to the language Python and the use of Python for data analysis such as text mining and sentiment analysis. It also introduces the principles behind FAIR data and explores ethical issues when working with open data.

Data Science is an exciting new area that combines scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Students taking this course are introduced to the most fundamental data analytic tools and techniques, learn how to use specialized software to analyze real-world data and answer policy-relevant questions.

This course introduces students to advanced statistics, applied to the biological sciences. It introduces more advanced linear and generalized linear models, as well as approaches to model building and comparison. It also covers applications of linear models to large-scale genomic data, programming, permutation-based tests, power analysis and multivariate statistics. In addition to providing the theoretical background of the approaches covered, the course puts much emphasis on practical implementation. Lectures are accompanied by weekly practical sessions in which students will work through analyses in the statistical software R, the standard in much of biological computing.