 | Study programme 2021-2022 | Français | |
 | Exploration et prédiction des données | ||
Programme component of Master's in Computer Science à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCINFO-022-M | Optional UE | GROSJEAN Philippe | S807 - Ecologie numérique |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 15 | 15 | 0 | 0 | 0 | 3 | 3.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-BIOG-025 | Science des données III : exploration et prédiction | 15 | 15 | 0 | 0 | 0 | Q1 | 100.00% |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
- Manage large-scale software development projects.
- Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
- Demonstrate independence and their ability to work alone or in teams.
- Manage research, development and innovation.
- Understand unprecedented problems in computer science and its applications.
- Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
- 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.
- Pursue further training and develop new skills independently.
- Develop and integrate a high degree of autonomy to evolve in new contexts.
- Apply scientific methodology.
- Critically reflect on the impact of IT in general, and on the contribution to projects.
- Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.
Learning Outcomes of UE
To be able to find useful information in a large dataset using data mining and machine learning tools , to analyze correctly biological data with time-dependencies and to analyse the spatial data. To be able to present results in a reproducible way (reports) and to use professional software in data science: R, RStudio, R Markdown, git.
Content of UE
The chapters of this UE are :
- Classification I - bases & LDA
- Classification II - metrics & trees methods
- Classification III = SVM, deep learning
- Time series I - description, ACF, spectral analysis
- Time series II - decomposition & regularisation
- Spatial statistics, maps & krigging
Prior Experience
Bases in data science, including project management, data importation and transformation, visualization of data through graphs and writing of reproducible reports. General uni- and multivariate statistics.
Type of Assessment for UE in Q1
- Presentation and/or works
- Practical test
- Graded tests
- eTest
Q1 UE Assessment Comments
Final grade made of different parts: - Continuous evaluation of the progression - Participation in flipped classes - Output during practical sessions - Evaluation of a report of data analysis - eTest
For the continuous evaluation of the progression, presence to the sessions is mandatory.
Type of Assessment for UE in Q3
- Presentation and/or works
- Practical Test
- Graded tests
- eTest
Q3 UE Assessment Comments
Similar to Q1.
Type of Resit Assessment for UE in Q1 (BAB1)
- N/A
Q1 UE Resit Assessment Comments (BAB1)
Not applicable.
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-BIOG-025 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-BIOG-025 |
|
Required Reading
| AA | |
|---|---|
| S-BIOG-025 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-BIOG-025 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-BIOG-025 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-BIOG-025 | Not applicable. |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-BIOG-025 | Barnier, J., 2018. Introduction à R et au tidyverse (https://juba.github.io/tidyverse/index.html). Ismay, Ch. & Kim A.Y, 2018. Moderndive: An introduction to statistical and data science via R (http://moderndive.com). Wickham, H. & Grolemund, G, 2017. R for data science (http://r4ds.had.co.nz). Zar, J.H., 2010. Biostatistical analysis (5th ed.). Pearson Education, London. 944pp. Dagnelie, P., 2007. Statistique théorique et appliquée, Volumes I et II (2ème ed.). De Boeck & Larcier, Bruxelles. 511pp (vol. I) 734pp (vol. II). Venables W.N. & B.D. Ripley, 2002. Modern applied statistics with S-PLUS (4th ed.). Springer, New York, 495 pp. Legendre, P. & L. Legendre, 1998. Numerical ecology (2nd ed.). Springer Verlag, New York. 587 pp. |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| S-BIOG-025 | Unauthorized |