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Establishment
Language of instruction
English
Teaching content
QUANTITATIVE METHODS
This course occurs in the following program(s)
MSc in Big Data Analytics for Business
- Crédits ECTS: 4.00
Training officer(s)
A.HOUDART
Stakeholder(s)
Adrien HOUDART
Présentation
Prerequisite
- The participants should have followed the courses Business Analytical Tools - Open Source and Business Analytical Tools - Commercial
Goal
At the end of the course, the student should be able to:
- describe, summarize and visualize company data
- describe the essential differences between descriptive and predictive analytics
- describe the process and the underlying steps needed to succeed in analytical projects
- present the essential concepts and output of analytical projects for a business audience
- spot predictive analytical opportunities to transform (big) data into improved business decisions
- use historical customer/company data to predict future events
These competencies and/or skills contribute to the following learning objectives
- 1.C Communicate effectively in English
- 3.A Breakdown complex organizational problems using the appropriate methodology
- 3.B Propose creative solutions within an organization
- 4.C. Convey powerful messages using contemporary presentation techniques
- 5.B Construct expert knowledge from cutting-edge information
- 7.D Be a reference point for expertise-related questions and ambiguities
- describe, summarize and visualize company data
- describe the essential differences between descriptive and predictive analytics
- describe the process and the underlying steps needed to succeed in analytical projects
- present the essential concepts and output of analytical projects for a business audience
- spot predictive analytical opportunities to transform (big) data into improved business decisions
- use historical customer/company data to predict future events
These competencies and/or skills contribute to the following learning objectives
- 1.C Communicate effectively in English
- 3.A Breakdown complex organizational problems using the appropriate methodology
- 3.B Propose creative solutions within an organization
- 4.C. Convey powerful messages using contemporary presentation techniques
- 5.B Construct expert knowledge from cutting-edge information
- 7.D Be a reference point for expertise-related questions and ambiguities
Presentation
This course introduces its participants to the fundamentals of descriptive and predictive analytics. Nowadays, there is a tremendous increase in customer/company information which is available to the analysts. Indeed, companies are collecting different types of information like purchasing behavior, complaint behavior, socio-demographic information, social media behavior etc. Consequently, knowing how to use this information to improve customer relationships is of high benefit for analysts. Through analytics, the analyst transforms raw data into better business decision. This course tries to fulfil the gap by reaching participants new ways to describe data and to proactively interact with customers on a one-to-one basis.
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Présentiel | |||
| Cours magistral | 24,00 | ||
| Travaux dirigés | 8,00 | ||
| Autoformation | |||
| Lecture du manuel de référence | 15,00 | ||
| Recherche | 13,00 | ||
| Travail personnel | |||
| Group Project | 25,00 | ||
| Charge de travail personnel indicative | 15,00 | ||
| Overall student workload | 100,00 | ||
Evaluation
The participants will be assessed on their skills and knowledge related to descriptive and predictive analytics. More specifically, students will be assessed on their participation during labs and tutorials, on their ability to apply the learned techniques in a group project and on their understanding of descriptive and predictive analytics in a written final exam.
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Contrôle continu | |||
| Participation | 13,00 | 1 | 20,00 |
| Autres | |||
| Projet Collectif | 10,00 | 1 | 40,00 |
| Examen (final) | |||
| Examen écrit | 2,00 | 0 | 40,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
Foster Provost, Tom Fawcett, Data Science for Business, 414p., English, O'Reilly, 2013, ISBN-13 978-1-4493-6132-7 -