![]() | Study programme 2020-2021 | Français | |
![]() | Exploration et prédiction des données | ||
Programme component of Master's in Computer Science à la Faculty of Science |
Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3 |
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Code | Type | Head of UE | Department’s contact details | Teacher(s) |
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US-M1-INFO60-060-M | Optional UE | GROSJEAN Philippe | S807 - Ecologie numérique |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
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| Français | 15 | 15 | 0 | 0 | 0 | 3 | 3.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
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S-BIOG-025 | Science des données III : exploration et prédiction | 15 | 15 | 0 | 0 | 0 | Q1 | 100.00% |
Programme component |
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Objectives of Programme's Learning Outcomes
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
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
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
Q3 UE Assessment Comments
Similar to Q1.
Type of Resit Assessment for UE in Q1 (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
Not applicable.
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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S-BIOG-025 |
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Mode of delivery
AA | Mode of delivery |
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S-BIOG-025 |
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Required Reading
AA | |
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S-BIOG-025 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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S-BIOG-025 | Not applicable |
Recommended Reading
AA | |
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S-BIOG-025 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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S-BIOG-025 | Not applicable. |
Other Recommended Reading
AA | Other Recommended Reading |
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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 |
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S-BIOG-025 | Unauthorized |