Single-Course English 5 ECTS

Computational Data Analysis

Overall Course Objectives

To provide the student knowledge of advanced computer intensive data analysis methods with applications to e.g. life sciences. These include problems with many variables and relatively few observations, images etc. The am is to give the student to analytically as well as based on computational results evaluate data analysis methods.

Learning Objectives

  • Evaluate clustering methods and select suitable parameters and models for given data
  • Evaluate linear discriminant analysis and ridge regression
  • Evaluate cross validation, bootstrapping and concepts such as overfitting.
  • Evaluate sparse regression and classification models.
  • Evaluate logistic regression and support vector classifiers for 2 class problems
  • Evaluate and interpret Classification and Regression Trees (CART).
  • Evaluate bagging, boosting and random forests for classification and regression.
  • Evaluate and interpret sparse latent methods such as sparse principal component and sparse partial least squares.
  • Evaluate a range of unsupervised decomposition methods
  • Compare the mentioned methods theoretically

Course Content

Methods: Clustering, ridge regression, elastic net, sparse principal components, sparse discriminant analysis and Gaussian mixture analysis, logistic regression, support vector machine, classification and regression trees, bagging, boosting, random forests, nonnegative matrix factorization, independent component analysis, sparse coding, archetypical analysis, multivay methods, functional data analysis.
Case studies: Varies each year.

Recommended prerequisites

02409/02450/27411, It suffices with one of the above mentioned courses. Alternatively a similar course in advanced statistics or data analysis. Knowledge of Matlab, R or Python is an advantage.

Teaching Method

Thirteen weeks of lectures and data bar exercises. The activities alternate between hands-on exercises and lectures. Additionally, two cases are carried out.

Limited number of seats

Minimum: 8, Maximum: 300.

Please be aware that this course has a minimum requirement for the number of participants needed, in order for it to be held. If these requirements are not met, then the course will not be held. Furthermore, there is a limited number of seats available. If there are too many applicants, a pool will be created for the remainder of the qualified applicants, and they will be selected at random. You will be informed 8 days before the start of the course, whether you have been allocated a spot.

See course in the course database.





13 weeks




DTU Lyngby Campus

Course code 02582
Course type Candidate
Semester start Week 5
Semester end Week 19
Days Thurs 8-12

7.500,00 DKK

Please note that this course has participants limitation. Read more