High-dimensional data analysis
|Period:||Semester 1, Block I, II|
- Yes Elective choice
- Yes Contractonderwijs
- Yes Exchange
- Yes Study Abroad
- No Evening course
- Yes A la Carte
- No Honours Class
This course gives an overview of statistical methods that are used for analyzing high- dimensional data sets in which many variables (often thousands) have been measured for a limited number of subjects. This type of data arises in genomics, where genetic information is measured for many thousands of genes simultaneously, but also in functional MRI imaging of the brain. The course covers the most important statistical issues in this field, which include: a) initial processing of the data; b) model- based differential expression analysis for Gaussian and count data (classical and Bayesian methods); c) multiple testing (family-wise error rate and false discovery rate control); d) penalized regression (lasso and ridge); e) shrinkage; and f) graphical models for constructing networks. Several specific types of high-dimensional data will be discussed and used during the course. Philosophy: Teaching students the adjustments to classical statistical methodology, necessary to tackle high-dimensional data.
Students should be able to perform and understand the most common analysis types on several types of high-dimensional data, and be familiar with the specific issues in important types of high dimensional data sets.
Mode of Instruction
The course consists of a series of lectures and practicals (partly computer practicals, partly exercises).
For the course days, course location and class hours check the Time Table under the tab “StatSci Students -> Program Schedule” at http://www.math.leidenuniv.nl/statscience
Date information about the exam and resit can be found in the Time Table pdf document under the tab “Masters Programme” at http://www.math.leidenuniv.nl/statscience. The room and building for the exam will be announced on the electronic billboard, to be found at the opposite of the entrance, the content can also be viewed here http://info.liacs.nl/math/.
Literature will be specified during course, no books are required.
Enroll in Blackboard for the course materials and course updates.
To be able to obtain a grade and the ECTS for the course, sign up for the (re-)exam in uSis ten calendar days before the actual (re-)exam will take place. Note, the student is expected to participate actively in all activities of the program and therefore uses and registers for the first exam opportunity.
Exchange and Study Abroad students, please see the Prospective students website for information on how to apply.
- This is an elective course in the Master’s programme of the specialisation Statistical Science for the Life & Behavioural sciences.
|Is part of||Programme type||Semester||Block|
|Statistical Science for the Life & Behavioural Sciences: Data Science||Master||1||I, II|
|Statistical Science for the Life and Behavioural Sciences||Master||1||I, II|