Statistics and Data Analysis for Computer and Systems Sciences
The course provides knowledge of classical and modern statistical methods for data analysis as well as its theoretical foundations. 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 using 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.
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Course structure
The course is part of the Master's program "Data Science, Statistics and Decision Analysis, 120 credits", which is given by the Department of Computer and Systems Sciences.
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course.
