Statistical consulting

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.

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 ...

Learning outcomes

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.

Content

Introduction to statistical practice. Problem oriented approaches to statistical work on problems presented by clients from research, business, or public organizations. Important elements:
- structuring projects with statistical content
- exploratory data analysis using effective visualizations
- challenges in statistical practice (missing values, outliers, transformations)
- communication with clients (meetings, presentations, reporting)
- professionalism (organization, planning, documenting, data privacy, intellectual property)
The center of the course consists of two real life case studies from different subject areas including medecine, psychology, industry, agriculture, management, and marketing.

Teaching methods

Mostly problem based learning
Two real projects
Reading of articles and sharing with the group
Exercises in data visualization, presentation and report writing

Evaluation methods

Evaluation based on the two real projects (statistical work, presentation, report writing)

Other information

A collection of articles in statistics, data science and neighboring disciplines will be distributed for reading and discussion.

Online resources

Moodle site.

Bibliography

Une série d'articles parus dans la littérature statistique récente est consacrée à cette problématique. Une liste détaillée sera remise aux étudiants.

Teaching materials

• matériel sur moodle

Faculty or entity

LSBA