Responsible Instructor 

Instructor 

Contact Hours / Week x/x/x/x 
0/0/3/0 (1 hr lab)

Education Period 

Start Education 

Exam Period 

Course Language 

Course Contents 
Mathematical aspects of problems related to data science. Problems from data science are approached from three different angles: Numerical Analysis, Probability Theory and Statistics. Subjects that will be covered include the Singular Value Decomposition (with applications to e.g. tomography), counterintuitive properties of random vectors in high dimensions, clustering, penalization and sparse data modeling.

Study Goals 
The student • Knows about how the challenge of huge amounts of data is viewed from various areas within mathematics (numerical analysis, applied probability, statistics) • Can solve wellformulated problems in mathematical models for big data (conceptually and numerically) • Can find, read and interpret scientific mathematics papers on the subject of big data and assess their relevance for a specific problem and apply the results • Can, together with fellow students in the group, pose relevant questions to problem owners based on a limited description of the problem • Can build a mathematical model for specific problems involving big data • Can report and present results for mixed audience, consisting of ‘problem owners’ and mathematicians

Education Method 
Lectures, (computer) exercise classes, working on applied problems consulting a staff member and a problem owner.

Literature and Study Materials 
Course slides, scientific papers proposed by lectureres

Assessment 
Biweekly home work (computer) exercises, data science project with presentation (all these in groups of 2  3 students) and a final (individual) oral exam.
