Scientific Computing for Data Analysis
Syllabus, Bachelor's level, 1TD352
- Code
- 1TD352
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Computer Science G2F, Technology G2F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 19 October 2023
- Responsible department
- Department of Information Technology
Entry requirements
60 credits including Algebra and Geometry/Linear Algebra and Geometry 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. Participation in Linear Algebra II/Linear Algebra for Data Analysis.
Learning outcomes
On completion of the course, the student should be able to:
- account for and perform tasks that require knowledge of the key concepts included in the course;
- describe, use and implement the algorithms included in the course;
- analyze the computational and memory complexity of different algorithms;
- solve technical and scientific problems given the mathematical model, by structuring the problem, choosing the appropriate numerical method, and generating a solution using software and your own code;
- present, explain, summarize, evaluate and discuss solution methods and results in a small report.
Content
Stochastic models, Monte-Carlo methods, Inverse Transform Sampling (ITS), stochastic simulation, Gillespies algorithm. Least square approximation with application to linear systems and regression models. QR factorization and orhogonalizations. Methods for eigenvalues and eigenvectors (power method and QR method). Singular value decomposition (SVD) and applications. Important key concepts included in the course include stochastic/deterministic model and method, matrix factorization, singular values and singular vectors.
Instruction
Lectures, laboratory exercises, problem solving sessions.
Assessment
Written exam (3 hp). Problem solving. Assignments with a written report (2 hp).
If there are special reasons for doing so, an examiner may make an exception from the method of
assessment indicated and allow a student to be assessed by another method. An example of special
reasons might be a certificate regarding special pedagogical support from the disability coordinator of the
university.