From big data to relevant information
3 ECTS Credits — Semester 1 — Elective
This is a field of study that focuses on using mathematical and statistical techniques to analyse and extract meaningful information from large datasets. These methods are designed to handle the challenges posed by big data, which typically involves massive, complex, and dynamic datasets.
Syllabus
This elective teaches how to extract relevant scientific information from experimental and simulated data. Modern statistical methods for processing and analysing large data streams (data mining, multivariate analysis, etc.) will be discussed. And introduction to AI and tools like ChatGPT is included.
Suggested bibliography
- Kutz, J. N., Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data, OUP
- Hair Jr, J. F; Black, W. C; Babin, B. J. and Anderson, R. E., Multivariate Data Analysis, Pearson.
Prerequisites
- Basic knowledge of probability and statistics,
- Basic knowledge of matrix algebra, eigenvectors/values.