Study programme 20182019  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) 

USB2SCBIOL006M  Compulsory UE  GROSJEAN Philippe  S807  Ecologie numérique des milieux aquatiques 

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 

SBIOG006  Data Sciences I : visualisation and inference  25  50  0  0  0  Q1  100.00% 
Programme component 

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; QQ plot; Boxplot; Histogram; Statistical population; Sampling; Inference; Probabilities; Statistic distribution; Central limit theorem; Confidence interval; Hypothesis test; Parametric and non parametric tests; Binomial, Poisson, Chi2, Normal, Student t and F distributions; Student ttest; One and two factors ANOVA; WilkoxonMannWithney test, KruskalWallis 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 

SBIOG006 

Mode of delivery
AA  Mode of delivery 

SBIOG006 

Required Reading
AA  

SBIOG006 
Required Learning Resources/Tools
AA  Required Learning Resources/Tools 

SBIOG006  Not applicable 
Recommended Reading
AA  

SBIOG006 
Recommended Learning Resources/Tools
AA  Recommended Learning Resources/Tools 

SBIOG006  Not applicable 
Other Recommended Reading
AA  Other Recommended Reading 

SBIOG006  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 

SBIOG006  Authorized 