Statistics

In this online course, learn how to construct graphs and visualizations according to the theory Grammar of Graphics. Learn how to create visualizations yourself using the software R and its package ggplot2. A central part of creating visualizations is making choices. Through the choices you make, your visualizations are more or less intelligible and also highlight different aspects of the data. An important element of the course is therefore to review visualizations by other course participants. Topics covered in the course include introduction to R and ggplot2; choice of color, symbols, scales, and perspective (2D, 3D); summation and abstraction; interactive visualizations; maps and spatial data; visualization of statistical models.

The course provides a precise and accurate treatment of introductory probability theory, statistical ideas, methods, and techniques.  Topics covered are data visualization and descriptive statistics, probability theory, random variables, common distributions of random variables, multivariate random variables, sampling distributions of statistics, point estimation, interval estimation, hypothesis testing, analysis of variance (ANOVA), linear regression, nonparametric tests, goodness-of-fit, and independence tests. 

This course introduces fundamental concepts of econometrics and data analysis that form the basis for data driven decision making, empirical analysis of causal relationships, and forecasting. It covers matrices and their use in linear regression analysis, probability distributions and their role in carrying out valid data approximations, and estimation methods and their importance in producing credible results of any data analysis. The course also introduces programming with R, which is the main programming language of statistical computing. It starts out with basic R operations and then, with time, students learn about ways to write their own functions in R.

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. At the end of the course the student has a wide knowledge of the most important statistical techniques employed for forecasting and prediction purposes in modern business activities. In particular the student is able to: select the most appropriate predictive model to solve the business problem at hand; analyze the data and perform predictions using the statistical software R; report the results in a proper format for the business management. The course content includes: a probabilistic approach to the business prediction and forecasting problem, evaluation of predictions and forecasts, linear predictors, and forecasting models.

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrolment is by permission of the instructor.

Climate change is no longer an abstract future threat. Human population is at the center of the climate system. A demographic perspective is hence critical for understanding, on the one hand, the impact of human activities on the global climate, and, on the other hand, the impacts of climate change on human population. Upon successful completing of this course, students have the knowledge and skills to: 1) demonstrate an understanding of how human population contributes to anthropogenic climate change taking into account demographic heterogeneity; 2) demonstrate an understanding of how anthropogenic climate change differentially affects human health, wellbeing and livelihoods; 3) critically evaluate and explain different scientific and statistical evidence employed to study the links between population dynamics and climate change; 4) conduct research through the consultation of academic literature and/or through the collection and analysis of data; 5) work in groups and develop class discussions. The course topics include:

• Introduction to population and climate change interactions
• Climate change and demographic heterogeneity (e.g. age, gender, education, income, locations)
• Population and energy consumption/carbon emissions
• Population, water, and food
• Climate change and health and mortality
• Climate change and family and fertility
• Climate change and migration
• Climate change and future population dynamics
• Date and methods for the study of population and climate change

This course extends the statistical ideas introduced in the first year to more complex settings. Mathematically, the central concept is the linear model, a framework for statistical modelling that accommodates multiple predictor variables, continuous and categorial, in a unified way. There is a focus on fitting models to real data from a variety of problem domains, using R to perform computations. 

The course covers methods for data mining and business analytics and their usage in making strategic business decisions. It concentrates on the modelling aspects of data mining and provides tools for better understanding key methods of data exploration, visualization, classification, prediction, and clustering. The course starts with data visualization and getting to know features hidden in the data. Traditional regression models and hypothesis testing are practiced using real data. This introduction to traditional approaches then leads to the discussion of more advanced methods such as, discriminant analysis, classification and clustering methods, which are useful in finding patterns hidden in the data. The course deals with various types of data such as categorical data, time series, text data, and network data, among others. The fundamentals of building suitable models are discussed. Illustrations are carried out using the statistical package R. 

 

Data science and machine learning are exciting new areas that combine 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. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. This course extends the foundation of probability and statistics with an introduction to the most important concepts in applied machine learning, with social science examples. It covers the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods covered.