 | Study programme 2017-2018 | Français | |
 | Datawarehousing and Datamining | ||
Programme component of Master's Degree in Computer Science à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-INFO60-005-M | Compulsory UE | WIJSEN Jozef | S832 - Systèmes d'information |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 30 | 0 | 30 | 0 | 0 | 6 | 6 | 2nd term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-INFO-027 | Datawarehousing and Datamining | 30 | 0 | 0 | 0 | 0 | Q2 | |
| S-INFO-888 | Datawarehousing and Datamining: Practical Work | 0 | 0 | 30 | 0 | 0 | Q2 |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Carry out development or innovation projects in IT.
- Master the complexity of such work and take into account the objectives and constraints which characterise it.
- Master communication techniques.
- Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
- Develop and integrate a high degree of autonomy.
- Develop and integrate a high degree of autonomy to evolve in new contexts.
- Apply scientific methodology.
- Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.
Learning Outcomes of UE
See the (unique) AA in this UE.
Content of UE
See the (unique) AA in this UE.
Prior Experience
Not applicable
Type of Assessment for UE in Q2
- Presentation and/or works
- Written examination
Q2 UE Assessment Comments
See the (unique) AA in this UE.
Type of Assessment for UE in Q3
- Presentation and/or works
- Written examination
Q3 UE Assessment Comments
See the (unique) AA in this UE.
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-INFO-027 |
|
| S-INFO-888 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-027 |
|
| S-INFO-888 |
|
Required Reading
| AA | |
|---|---|
| S-INFO-027 | |
| S-INFO-888 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-INFO-027 | Web site with course notes, slides, and past examinations. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining. Addison-Wesley 2005, ISBN 0-321-32136-7 (chapters available online) Weka data mining software |
| S-INFO-888 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-INFO-027 | |
| S-INFO-888 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-INFO-027 | Not applicable |
| S-INFO-888 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-INFO-027 | Not applicable |
| S-INFO-888 | Not applicable |
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term