 | Study programme 2021-2022 | Français | |
 | Science des données I : visualisation et inférence | ||
Programme component of Bachelor's in Mathematics à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-B3-SCMATH-035-M | Optional UE | GROSJEAN Philippe | S807 - Ecologie numérique |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 0 | 70 | 0 | 0 | 0 | 9 | 9.00 | Année |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-BIOG-006 | Data Sciences I : visualisation | 0 | 35 | 0 | 0 | 0 | Q1 | |
| S-BIOG-027 | Data Science I: Inference | 0 | 35 | 0 | 0 | 0 | Q2 |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Understand "elementary" mathematics profoundly
- Understand the fundamentals of probability and statistics
- Solve new problems
- Use knowledge from different fields to address issues
- Use computers effectively
- Use at least one programming language
- Develop computer programs to solve problems with mathematical formulation
- Address literature and interact within other scientific fields
- Have a good knowledge of related fields using mathematics
Learning Outcomes of UE
To master software and statistical tools required for data science, more particularly, data importation, management and transformation, data visualization and inference. To present results clearly and adequately in a scientific report. To be able to analyze correctly usual biological data in practice.
Content of UE
The chapters of this UE are:
- Introduction - Software & tools (Software R, RStudio, git & Markdown)
- Visualisation I - Scatterplot
- Visualisation II - Distributions
- Visualisation III - Barplot/boxplot
- Data processing I - Importation/conversion
- Data processing II - Contingency/sampling
- Data processing III - Multi-tables/databases
- Experimental design & good practices
- Probabilities & distributions
- Chi-2 test, proportions & correlations
- Confidence interval/Student test
- Analysis of variance
Prior Experience
Basic use of a computer. Bases in calculus, including logarithm and exponential, cartesian coordinate system and elementary geometry in 2D and 3D.
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 Q2
- Presentation and/or works
- Practical test
- Graded tests
- eTest
Q2 UE Assessment Comments
Similar to Q1.
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-006 |
|
| S-BIOG-027 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-BIOG-006 |
|
| S-BIOG-027 |
|
Required Reading
| AA | |
|---|---|
| S-BIOG-006 | |
| S-BIOG-027 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-BIOG-006 | Not applicable |
| S-BIOG-027 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-BIOG-006 | |
| S-BIOG-027 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-BIOG-006 | Not applicable |
| S-BIOG-027 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-BIOG-006 | 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). Cornillon, P.A. Et al, 2008. Statistiques avec R. Presses Universitaires de Rennes. 257pp. Dagnelie, P., 2007. Statistique théorique et appliquée, Volumes I et II (2ème ed.). De Boeck & Larcier, Bruxelles. 511pp (vol. I) 734pp (vol. II). |
| S-BIOG-027 | 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. |