| Course label : | Business Decision |
|---|---|
| Teaching departement : | MIN / Applied Mathematics and General Computing |
| Teaching manager : | Mister CHRISTOPHE SUEUR |
| Education language : | French |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | IE4_ADAD_MIN_BDE - Business Decision |
Education team
Teachers : Mister CHRISTOPHE SUEUR
External contributors (business, research, secondary education): various temporary teachers
Summary
This teaching presents data table analysis techniques (multivariate analysis, data reduction) as well as an introduction to the concept of "Data Mining".
Educational goals
At the end of the course, the student will be able to: - Analyze data tables (multivariate analysis) - Perform an analysis to find trends or correlations among large masses of data - Detect strategic information or new knowledge - Understand the concept of "Data Mining" Contribution of the course to the competency framework; At the end of the course, the student will have progressed in: - Analyze and put in place a scientific approach of problem solving - Bring a solution to a problem - Set up test protocols - Make and run test games - Analyze and implement a scientific approach to solving complex projects - Team working Knowledge worked: Part I: Reminders Notion of mean, variance, standard deviation, correlation, chi-square Part II Data Representation Notion of variables-individuals Notion of tables of data (tables individuals-characters, table of contingency ...) Part III: Presentation of analytical techniques ACP Principal Components Analysis Classification Canonical Analysis Discriminant Analysis Correspondance Factorial Analysis AFC Anova Part IV: Data Mining Introduction to Datamining Case study with R (open source software dedicated to Data Analysis) Skills developed: - Analyze and put in place a scientific approach of problem solving - Bring a solution to a problem - Set up test protocols - Make and run test games
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments:
Online resources
Using the R software. Many online resources are available for free.
Pedagogy
Teaching mainly in the classroom, in the form of TD courses.
Sequencing / learning methods
| Number of hours - Lectures : | 8 |
|---|---|
| Number of hours - Tutorial : | 8 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 4 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Basic knowledge of statistics and linear algebra (matrices, vector spaces)