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DESCRIPTIVE & PREDICTIVE ANALYTICS

2017-2018

IESEG School of Management ( IÉSEG )

Code Cours :

1718-IÉSEG-MBD1S2-QMS-MBDCI01UE

QUANTITATIVE METHODS


Niveau Année de formation Période Langue d'enseignement 
MSc in Big Data Analytics for Business1S2English
Professeur(s) responsable(s)G.VERSTRAETEN
Intervenant(s)Dr. Nele Verbiest


Pré requis

The participants should have followed the courses Business Analytical Tools - Open Source and Business Analytical Tools - Commercial

Objectifs du cours

At the end of the course, the student should be able to:
• describe the essential differences between descriptive and predictive analytics
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, (for example customer behavior)

Contenu du cours

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 d'enseignement

Organisation du cours

TypeNombre d'heuresRemarques
Face to face
lecture24,00  
Tutorials8,00  
Independent work
Reference manual 's readings15,00  
Research13,00  
Independent study
Group Project25,00  
Estimated personal workload15,00  
Charge de travail globale de l'étudiant100,00  

Méthodes pédagogiques

  • Tutorial
  • Presentation
  • Research
  • Project work
  • Interactive class
  • Case study
  • Coaching


Évaluation

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.

Type de ContrôleDuréeNombrePondération
Continuous assessment
Participation13,00120,00
Others
Group Project8,00140,00
Final Exam
Written exam2,00040,00
TOTAL     100,00

Bibliographie

  • Foster Provost, Tom Fawcett, Data Science for Business, 414p., English, O'Reilly, 2013, ISBN-13 978-1-4493-6132-7 -




 
* Informations non contractuelles et pouvant être soumises à modification
 
 
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