Course: Data Science 3 credits: 5
- Course code
- BFVM23DATASCNC3
- Name
- Data Science 3
- Study year
- 2023-2024
- ECTS credits
- 5
- Language
- English
- Coordinator
- F. Feenstra
- Modes of delivery
-
- Lecture
- Assessments
-
- Linear Algebra - Written, organised by STAD examinations
- Signal Analysis - Written, organised by STAD examinations
Learning outcomes
You manipulate mathematical expressions involving real and complex numbers, scalars, vectors and matrices.
You invert and decompose matrices, assisted by computer, and diagnose and solve rank-deficient or ill-conditioned problems.
You process time-series and image data, including visualization, resampling and interpolation.
You apply linear filters and other data transformations in both the time- and frequency domains.
Content
This course introduces the fundamental concepts and techniques of linear algebra and their applications in solving problems in different areas. The course begins with an introduction to complex numbers, vectors and matrices and how to operate on these. It includes topics such as matrix determinants and trace, matrix inversion and decomposition, and characterization of matrix rank.
In parallel, the course covers signal analysis. Topics covered in this section include interpolation and curve fitting, windowing, filtering and convolution, Fourier transformation, and discrete filter design. Overall students learn to analyze and manipulate a wide range of time series and image data to identify patterns, remove noise, and enhance signals.
Included in programme(s)
School(s)
- Institute for Life Science & Technology