Duration
30h Th, 15h Pr, 30h Proj.
Number of credits
| Master of Science (MSc) in Data Science | 5 crédits | |||
| Master of Science (MSc) in Data Science and Engineering | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The course is devoted to the following themes:
- Exploratory data analysis
- Estimation of the covariance matrix: classic technique, penalized version and robust version
- Dimension reduction technique: Principal Component Analysis, Multidimensional Scaling, tSNE
- Supervised classification: discriminant analysis and logistic regression
- Multivariate ranks and quantiles
Learning outcomes of the learning unit
The student will gain sufficient knowledge to be able to select the appropriate multivariate technique to reduce the dimension of the problem or construct classification rules,...
This course contributes to the learning outcomes I.1, I.2, I.3, II.1, II.2, III.1, III.2, IV.1, IV.4, VI.1, VI.2, VII.3, VII.4 of the MSc in data science and engineering.
Prerequisite knowledge and skills
A strong background in univariate statistics is required. Moreover, even though the mathematical justifications are not developped in details, the students must be familiar with the basic notions of linear algebra (vector, matrix, determinant, eigen values and eigen vectors...).
Planned learning activities and teaching methods
The theory is exposed in an ex-cathedra way. During the practicals, the students will work by themselves.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
The course is officially scheduled on Wednesday PM in the first semester and is organised face-to-face.
Recommended or required readings
There are no lecture notes. The slides will be available from eCampus. Moreover, for each theme, a reference book will be notified in order to suggest additionnal reading.
Assessment methods and criteria
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Written work / report
Additional information:
The final grade is a weighted mean computed on the grades obtained for
- the personal homeworks given during the semester
- the written exam consisting of a data analysis and simulations
Work placement(s)
Organizational remarks
The lectures are taught in English.
Contacts
Lecturer: Gentiane HAESBROECK, Institute of Mathematics (B37), g.haesbroeck@ulg.ac.be