![]() | Study programme 2018-2019 | Français | |
![]() | Data Sciences I : visualisation and inference | ||
Programme component of Bachelor's Degree in Biology 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 des milieux aquatiques |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Français | 25 | 50 | 0 | 0 | 0 | 6 | 6.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
S-BIOG-006 | Data Sciences I : visualisation and inference | 25 | 50 | 0 | 0 | 0 | Q1 | 100.00% |
Programme component |
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Objectives of Programme's Learning Outcomes
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
Software R, RStudio, git & Markdown. Importation and transformation of datasets. Visualisation of uni-, bi-, and multivariate data. Descriptive statistics; Mean; Median; Standard deviation; Variance; Q-Q plot; Boxplot; Histogram; Statistical population; Sampling; Inference; Probabilities; Statistic distribution; Central limit theorem; Confidence interval; Hypothesis test; Parametric and non parametric tests; Binomial, Poisson, Chi-2, Normal, Student t and F distributions; Student t-test; One and two factors ANOVA; Wilkoxon-Mann-Withney test, Kruskal-Wallis test; Correlation; Pearson; Spearman.
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
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 - Written exam
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)
Néant
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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S-BIOG-006 |
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Mode of delivery
AA | Mode of delivery |
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S-BIOG-006 |
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Required Reading
AA | |
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S-BIOG-006 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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S-BIOG-006 | Not applicable |
Recommended Reading
AA | |
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S-BIOG-006 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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S-BIOG-006 | 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). |
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-006 | Authorized |