Business Data Analytics 1

Ects : 3

Enseignant responsable :

  • JEAN AXEL ULLERN
  • JEAN FRANCOIS BONNET

Volume horaire : 18

Description du contenu de l'enseignement :

This course, intended for all MIB Master's students, aims to provide them with an understanding of the fundamentals of data science, data analysis, and AI applied to business issues. It uses the Dataiku platform to illustrate concepts and enable practical application.

 

Course Structure

 

Main topics covered:

1. Introduction to Data Science & Business Analytics

  • The role of data in businesses
  • Basics of business intelligence
  • Key concepts and challenges (decision-making, automation, AI, etc.)
  • Concrete examples of sector-specific applications

2. Review of statistics & fundamentals of data analysis

  • Measures of central tendency and dispersion
  • Concepts of correlation and causality
  • Basics of applied probability
  • Review of matrices/vectors/scalars/tensors

3. Data visualization & exploration

  • Importance of visualization for decision-making
  • Tools and best practices (curves, histograms, heat maps, etc.)

4. Data preparation and transformation

  • Data cleaning, missing value management
  • Aggregations, joins, data set enrichment

5. Introduction to machine learning & predictive models**

  • Logic of supervised and unsupervised algorithms
  • Use case examples (customer attrition prediction, segmentation, anomaly detection)

6. Model performance evaluation

  • Metrics and KPIs
  • Limitations and interpretation of results

Pré-requis obligatoires :

To ensure that students fully benefit from the course from the very first session, they are advised to:

? Have a good grasp of the basics of descriptive statistics (mean, median, standard deviation, normal distribution)

? Be familiar with Excel and basic data visualization tools (e.g., pivot tables)

? Understand the fundamental principles of machine learning (supervised and unsupervised learning concepts)

To validate these prerequisites, students may take a short online training course (MOOC recommended) on the basics of data analysis (free courses) before the start of the course in the 1st semester.

Coefficient : 1

Compétence à acquérir :

Data analytical capabilities