Big Data Analytics


Big Data Analytics
Quantitative methods / Statistics
Contact hours
27 H
Number of spots
Open to visitors

Pedagogical contribution of the course to the program

Développer un management à impact grâce aux connaissances et aux outils les plus récents dans les domaines du management

Developing a strategic and managerial vision in a complex, uncertain and changing environment
Evaluate sustainable managerial practices using managerial concepts and instruments as well as digital tools
Design solutions adapted to organizational problems by applying relevant methodologies

Développer des compétences managériales de niveau avancé se traduisant par un leadership responsable

Co-build a managerial and organizational culture through collaborations and team projects
Recommend decision making by taking a critical approach to driving change in organizations

Pratiquer un management à impact dans un environnement multiculturel et international, porté par un "European mindset"

Communicate in a professional context in (foreign) languages, in writing and/or orally


The purpose of this course is to provide students with an overview of theoretical fundamentals and practical cases regarding the use of Big Data, Artificial Intelligence, Machine Learning, and Analytics in Business, Management, and organizations. This course investigates the new Big Data and Analytics (Artificial Intelligence, Machine Learning, Business Intelligence, Business Object, etc.) in today's modern Management in business organizations. This course's set of knowledge will not be restricted to academic notions but covers a set of real-world challenging case studies, success, and fail stories in Data and AI use. The goal of this course is to offer hands-on applied experience in apprehending methodologies and solutions. INTENDED OUTCOMES Upon successful completion of this course, students will be able to: Understand the paradigm of Big Data and Analytics, and the related concepts such as Data sources, Artificial Intelligence, Internet of Things, Machine Learning, Business Intelligence, etc., Discover the approaches of analytics that are adapted by the organizations, Research and use the concepts and trends underlying current and future methods of Big Data Analytics in Management of organizations, Appraise management cases where managers are able to apply Big Data Analytics in order to facilitate decision making in Management, Understand, control, plan, and evaluate a Big Data Project in Management with the use of Analytics.

Teaching methods


- Lectures
- Tutorials

In group

- Exercises
- Oral presentations
- Projects
- Case studies/texts


- Discussions/debates


No items in this list have been checked.

Learning objectives

Cognitive domain

Upon completion of this course, students should be able to
  • - (level 3) manipulate the business data collection and acquisition in data bases
  • - (level 4) analyze the situation about data organization and processing in the organization
  • - (level 4) audit the data pipeline in the organization
  • - (level 5) evaluate the maturity level of an organization for Big Data implementation

Affective domain

Upon completion of this course, students should be able to
  • - (level 4) formulate a combination of data sources in an integrated Big Data System (example of MapReduce query)
  • - (level 4) prepare a study of a situation to justify the need a Big Data system in an organization
  • - (level 5) question the big data base to extract valuable information (example of BigQuery)
  • - (level 5) exemplify an integrated system of Big Data for an organization


Part 1: Big Data Load (hours) 1.1. Data Concept 1 1.1.1. Data 1.1.2. Information 1.1.3. Knowledge 1.2. Big Data Concept 1 1.2.1. Volume 1.2.2. Variety 1.2.3. Velocity 1.2.4. Veracity 1.3. Big Data Life Cycle 3 1.3.1. Data Acquisition Multi-Channels Internet of Things Cloud/Grid Computing 1.3.2. Data Storage Data Base Data Warehouse Data Lake Data Security Part 2: Analytics 2.1. Concept 1 2.2. Types 6 2.2.1. Traditional Analytics Business Intelligence (BI) Online Analytical Processing (OLAP) 2.2.2. Advanced Analytics Artificial Intelligence Data Mining Machine Learning 1. Supervised Learning Algorithms 2. Unsupervised Learning Algorithms 3. Reinforcement Learning Algorithms 4. Ensemble Learning Algorithms 5. Deep Learning Algorithms Part 3: Big Data Analytics 3.1. Concept 1 3.2. Role 1 3.3. BDA As a field 1 3.4. BDA architecture 1 3.5. Big Data Analytics Life Cycle 3 3.5.1. Data Processing Hadoop MapReduce 3.5.2. Data Quality 3.5.3. Data Visualization 3.5.4. Data Compliance 3.5.5. Data Preparation 3.6. Big Data Analytics Project 1 3.6.1. Business Value 3.6.2. Data Features 3.6.3. From Business Problem to Analytics Solution 3.7. Organizational and Social Mutations 1 3.9. Applications In Management: Real-world Case Studies (conditioned by the progress in the class) 4 3.9.2. In Insurance: Prediction of Insurance Fraud 3.9.4. In Transversal Management: Predictive Business Process Management 3.9.6. In Supply Chain Management: Predictive Risk Management 3.9.5. In Marketing: Chat bots, Marketing automation and Market Segmentation 3.9.7. In CRM: Prediction of Customer Churn

No prerequisite has been provided

Knowledge in / Key concepts to master

Requires a background in information systems, basics in Management, enterprise systems, and MS Office tools such as MS Excel.

Teaching material

Mandatory tools for the course

- Computer
- Other : Videos (Youtube)

Documents in all formats

- Case studies/texts
- Worksheets

Moodle platform

- Upload of class documents
- Interface to submit coursework
- Assessments
- Coaching/mentoring


No items in this list have been checked.

Additional electronic platforms

No items in this list have been checked.

Recommended reading

Book: ‘Data Analytics Made Accessible’. 2018. by Anil Maheshwari Book: ‘Too Big to Ignore: The Business Case for Big Data’. by award-winning Book: ‘Data Smart: Using Data Science to Transform Information into Insight’, by J. W. Foreman’.

Paper: ‘Almeida, F. (2018). Big Data: Concept, Potentialities and Vulnerabilities’. Emerging Science Journal, 2(1). McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68. Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media. Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387-394.

EM Research: Be sure to mobilize at least one resource

Textbooks, case studies, translated material, etc. can be entered
1. Chehbi-Gamoura, S., et al. (2020). Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model. Production Planning & Control, 31(5), 355-382. 2. Chehbi-Gamoura, S., et al. (2020). Cross-management of risks in big data-driven industries by the use of fuzzy cognitive maps. Logistique & Management, 28(2), 155-166. 3. Chehbi-Gamoura, S., and Malhotra M. (2020). Master Data-Supply Chain Management, the Key Lever for Collaborative and Compliant Partnerships in Big Data Era: Marketing/Sales Case Study. Impacts and Challenges of Cloud Business Intelligence, New York, USA, IGI Global, 72-101.


List of assessment methods

Intermediate assessment / continuous assessment 1Class no. 4
Written and oral (180 Min.) / Group / English / Weight : 15 %
Details : 3 heures de travail en groupe pour préparer un sujet dans une liste fournie en lien avec le cours. Un plan obligatoire est imposé. Tous les étudiants de chaque groupe doivent présenter sous forme de pitch une présentation orale en 15min avec questions/réponses. Le support de la présentation est également fourni en complément. Tout le travail se fait en anglais.
This evaluation is used to measure ILO1.1-PGE, ILO1.2-PGE, ILO1.3-PGE, ILO2.1-PGE, ILO2.2-PGE, ILO4.1-PGE, ILO1.1-PGE, ILO1.2-PGE, ILO1.3-PGE, ILO2.1-PGE, ILO2.2-PGE, ILO4.1-PGE, ILO1.1-PGE, ILO1.2-PGE, ILO1.3-PGE, ILO2.1-PGE, ILO2.2-PGE, ILO4.1-PGE, ILO1.1-PGE, ILO1.2-PGE, ILO1.3-PGE, ILO2.1-PGE, ILO2.2-PGE, ILO4.1-PGE, ILO1.1-PGE, ILO1.2-PGE, ILO1.3-PGE, ILO2.1-PGE, ILO2.2-PGE, ILO4.1-PGE, ILO1.1-PGE, ILO1.2-PGE, ILO1.3-PGE, ILO2.1-PGE, ILO2.2-PGE, ILO4.1-PGE, ILO1.1-PGE, ILO1.2-PGE, ILO1.3-PGE, ILO2.1-PGE, ILO2.2-PGE, ILO4.1-PGE
Intermediate assessment / continuous assessment 2Other (date, pop quiz, etc.) : chaque fin de séance
Written (45 Min.) / Individual / English / Weight : 15 %
Details : Test automatisé via Moodle de validation des acquis de chaque séance. Le test est ouvert à la suite de chaque séance pendant une semaine. Les questions du test incite l'étudiant à replonger dans son cours de la séance pour pouvoir répondre. C'est un manière de combattre le décrochage chez les étudiants et de progresser en validant chaque étape.
This evaluation is used to measure ILO1.3-PGE
Final evaluationLast class
Written (120 Min.) / Individual / English / Weight : 70 %
Details : Examen final écrit. Il contient des exercices pratiques et des problèmes à résoudre ainsi que des questions de compréhension sur tout le contenu du cours.
This evaluation is used to measure ILO1.2-PGE, ILO2.1-PGE, ILO2.2-PGE, ILO4.1-PGE
No assessment methods have been attributed to this course yet.