 | Study programme 2019-2020 | Français | |
 | Big Data Analytics | ||
Programme component of Master's in Mathematics à la Faculty of Science |
| Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3 |
|---|
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCMATH-055-M | Optional UE | BEN TAIEB Souhaib | S861 - Big Data and Machine Learning |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Anglais, Français | 30 | 30 | 0 | 0 | 0 | 6 | 6.00 | 2nd term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-INFO-075 | Big Data Analytics | 30 | 30 | 0 | 0 | 0 | Q2 | 100.00% |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Carry out major projects.
- Work in teams and, in particular, communicate effectively and with respect for others.
- Present the objectives and results of a project orally and in writing.
- Communicate clearly.
- Communicate the results of mathematical or related fields, both orally and in writing, by adapting to the public.
- Adapt to different contexts.
- Have developed a high degree of independence to acquire additional knowledge and new skills to evolve in different contexts.
- Critically reflect on the impact of mathematics and the implications of projects to which they contribute.
Learning Outcomes of UE
See single learning activity.
Content of UE
This unit is a follow-up to the "Machine Learning" unit. It covers other topics related to big data analysis which will allow to deepen the knowledge about the previously covered topics, such as "support vector machines", le "deep learning", etc.
Prior Experience
"Machine learning" or "Big Data Analytics I" is a pre-requisite for this unit.
Type of Assessment for UE in Q2
- Presentation and/or works
- Written examination
- Graded tests
Q2 UE Assessment Comments
Written exam (60% of total score)
Project (20% of total score)
Assignments (20% of total score)
There is a hurdle of 50% for each of the previous evaluations
Type of Assessment for UE in Q3
- Presentation and/or works
- Oral examination
- Graded tests
Q3 UE Assessment Comments
Oral exam (60% of total score)
Project (20% of total score)
Assignments (20% of total score)
There is a hurdle of 50% for each of the previous evaluations
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-INFO-075 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-075 |
|
Required Reading
| AA | |
|---|---|
| S-INFO-075 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-INFO-075 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-INFO-075 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-INFO-075 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-INFO-075 | Not applicable |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| S-INFO-075 | Unauthorized |