60 credits including Algebra and Geometry/Linear Algebra and Geometry I/Linear algebra I. Participation in a programming course in Python (for example Computer Programming I). Participation in one of the courses Introduction to Scientific Computing, Scientific Computing I, or Statistical Machine Learning. Participation in Probability and Statistics or Mathematical Statistics KF. Participation in Linear Algebra II/Linear Algebra for Data Analysis/Geometry and Calculus II.
This course focuses on handling large amounts of data and is divided into three different blocks. The first block deals with stochastic simulations, the second with regression analysis and least squares methods and the third with eigenvalue problems, singular value decomposition and principal component analysis. In the field of data analysis and machine learning, many algorithms and applications are based on the methods covered in this course. We study the computational methods used when working practically with data analysis of large amounts of data.
