5 credits
30.0 h
Q1 and Q2
Teacher(s)
Ritter Christian;
Language
English
Prerequisites
Professional integration activity
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Main themes
- Exploratory data analysis and rendering of data by tables and graphs - practical issues in data analysis (missing values, outliers, transformations) - review of common statistical analysis methods (regression, ANOVA, multivariate analysis; choice depends on selected projects) - communication with clients (project discussions, presentation of results, report writing) - professional and ethical conduct (analysis plan and cost estimation, mutual responsibilities of statistician and client, truthful representation, guidelines for ethical conduct) - practical problem solving in two real life cases coming from diverse application areas including medecine, psychology, engineering, agronomy and business ...
Aims
At the end of this learning unit, the student is able to : | |
1 | The participants in this course will acquire knowledge and skill in three areas: - statistical analysis of real life data (from problem method), - communication (discussion with clients, oral and written presentation of results), - aspects of professionalism and ethical conduct (planning, cost, good practice) To accomplish these objectives, the participants will work on two real life consulting projects and their evaluation provides the main part of their grade. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- Exploratory data analysis and rendering of data by tables and graphs - practical issues in data analysis (missing values, outliers, transformations) - review of common statistical analysis methods (regression, ANOVA, multivariate analysis; choice depends on selected projects) - communication with clients (project discussions, presentation of results, report writing) - professional and ethical conduct (analysis plan and cost estimation, mutual responsibilities of statistician and client, truthful representation, guidelines for ethical conduct) - practical problem solving in two real life cases coming from diverse application areas including medecine, psychology, engineering, agronomy and business ...
Bibliography
Une série d'articles parus dans la littérature statistique récente est consacrée à cette problématique. Une liste détaillée sera remise aux étudiants.
Faculty or entity
LSBA
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
Master [120] in Statistics: Biostatistics
Master [120] in data Science: Statistic
Master [120] in data Science: Information technology