Statistics

What puts former criminals on the right track? How can we prevent heart disease? Can Twitter predict election outcomes? What does a violent brain look like? How many social classes does 21st century society have? Are hospitals spending too much on health care, or too little? Data analysis is the art and science of tackling questions like these by looking at data. Just as cartographers make maps to see what a country looks like, data analysts explore the hidden structures of data by creating informative pictures and summarizing relationships among variables. And just as doctors diagnose sick patients and advise healthy ones on how to stay healthy, data analysts predict important events and variables so we can act on this knowledge. Methods from statistics, machine learning, and data mining play an important part in this process, as well as visualizations that allow the analyst and other humans to better understand what we can conclude from the available facts. During this course, students actively learn how to apply the main statistical methods in data analysis and how to use machine learning algorithms and visualizing techniques. The course goes beyond linear and logistic regression and thus continue where “Fundamental techniques in data science with R” ended. The course has a strongly practical, hands-on focus: rather than focusing on the mathematics and background of the discussed techniques, student gain hands on experience in using them on real data during the course and interpreting the results. Entry requirements include at least followed an introductory statistics course of 7.5 EC, and familiarity with correlation and regression, comparing means and cross tabulations of categorical variables. It's also expected to have hands on experience in carrying out these analyses, with, for example, SPSS, Stata, R or SAS.

This course covers: introduction to actuarial modelling; the application of compound interest techniques to financial transactions; generalized cash-models to describe financial transactions such as zero-coupon bonds, fixed interest securities, cash on deposit, equities, interest only loans, repayment loans, annuities certain and others; introduction to R programming for Actuarial Science, and introduction to life insurance. 

This course focuses on data about connections, forming structures known as networks. Networks and network data describe an increasingly vast part of the modern world, through connections on social media, communications, financial transactions, and other ties. This course covers the fundamentals of network structures, network data structures, and the analysis and presentation of network data. Students work directly with network data, and structure and analyze these data using the R statistical programming language. This course develops the theory and methodological tools needed to model and predict social networks and use them in social sciences as diverse as sociology, political science, economics, health, psychology, history, or business. The core of the course comprises the essential tools of network analysis, from centrality, homophily, and community detection, to random graphs, network formation, and information flow.