Central is understanding of the entire process of analysis from data sources and data collection, data management, estimation, inference, prediction and practical applications. Great emphasis is placed on practical data handling, visualization and analysis through programming in R. Throughout, emphasis is placed on a critical approach when using statistical methods and interpreting results.

The course covers:

• data collection methods and data sources,
• different data types such as numerical and categorical but also text, image and spatial data,
• graphical and numerical descriptions of data,
• regression analysis: models with one and more explanatory variables, assumptions, estimation, inference, prediction, model evaluation. Time series analysis and forecasting. The connection to modern data analysis methods such as machine learning is addressed.
• probability theory: basic concepts, probability models, discrete and continuous random variables, probability distributions, expected value and variance, covariance and correlation, some different standard distributions, linear combinations of several random variables, sampling distributions and the central limit theorem.
• statistical inference: point and interval estimation, hypothesis testing, p-values and prediction, and introduction to likelihood.

The course is given by the Department of Statistics.