Study programme 2023-2024 | Français | ||
Data Sciences I : visualisation and inference | |||
Programme component of Bachelor's in Biology (MONS) (day schedule) à la Faculty of Science |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
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US-B2-SCBIOL-006-M | Compulsory UE | GROSJEAN Philippe | S807 - Ecologie numérique |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Français | 0 | 70 | 0 | 0 | 0 | 6 | 6.00 | 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 |
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Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
To understand and to be able to use 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.
UE Content: description and pedagogical relevance
The pedagogical material is available online: https://wp.sciviews.org. The chapters of this UE are:
- Introduction - Software & tools (Software R, RStudio, git & Markdown)
- Visualisation I - Scatterplot and R Markdown (Quarto)
- Visualisation II - Histogram, density plot, violin plot
- Visualisation III - Barplot, piechart, boxplot, plots assemblage
- Data processing I - Importation, conversion, dplyr
- Data processing II - Contingency, sampling, multi-table processing with tidyr
- Probabilities & distributions
- Chi-2 test
- Confidence interval/Student test
- One-way analysis of variance
- Two-way analysis of variance
- Correlation and correlation tests
Prior Experience
Basic use of a computer. Bases in calculus, including logarithm and exponential, cartesian coordinate system and elementary geometry in 2D and 3D. Resources to update your prior knowledges: https://www.khanacademy.org/math, math 1, 2 & 3 + geometry, also https://edu.gcfglobal.org/en/computerbasics/ to learn the basics of computers, and possibly https://edu.gcfglobal.org/en/typing/ to learn typing on a keyboard.
Type(s) and mode(s) of Q1 UE assessment
Q1 UE Assessment Comments
Grading is done by using an ongoing evaluation of the progression all along the sessions. Presence to the sessions is mandatory. Unjustified absences will be sanctionned.
Method of calculating the overall mark for the Q1 UE assessment
The final grade is the average of the grade for Q1 and the grade for Q2 (50/50) -grades for ongoing assessments, see corresponding AA-. The grade for each AA must be at least 8/20, or the weakest grade is used as UE final grade. In case of failure, both AA must be done again next year.
Type(s) and mode(s) of Q1 UE resit assessment (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
Not applicable.
Method of calculating the overall mark for the Q1 UE resit assessment
Not applicable.
Type(s) and mode(s) of Q2 UE assessment
Q2 UE Assessment Comments
Grading is done by using an ongoing evaluation of the progression all along the sessions. Presence to the sessions is mandatory. Unjustified absences will be sanctionned.
Method of calculating the overall mark for the Q2 UE assessment
The final grade is the average of the grade for Q1 and the grade for Q2 (50/50) -grades for ongoing assessments, see corresponding AA-. The grade for each AA must be at least 8/20, or the weakest grade is used as UE final grade. In case of failure, both AA must be done again next year.
Type(s) and mode(s) of Q3 UE assessment
Q3 UE Assessment Comments
Given that the grade for this UE is established through ongoing assessment of works that cannot be organized during the summer, there is no second session.
Method of calculating the overall mark for the Q3 UE assessment
Not applicable
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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S-BIOG-006 |
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S-BIOG-027 |
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Mode of delivery
AA | Mode of delivery |
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S-BIOG-006 |
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S-BIOG-027 |
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Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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S-BIOG-006 | The content for this course is available online https://wp.sciviews.org |
S-BIOG-027 | The content for this course is available online https://wp.sciviews.org |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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S-BIOG-006 | Not applicable |
S-BIOG-027 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
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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. |