Visualisation plan de cours 2026 / 2027

EM09HMBD

Big Data Analytics & Artificial Intelligence with Python

Programme

PGE PGE4 FI

Semestre

Coefficient

Volume horaire

Face à face : 25 H

Travail personnel indicatif : 50 H

Discipline

Quantitative methods / Statistics

Nombre de places

Cours ouvert aux étudiants visitants

Oui

Langue d'enseignement

Anglais

Responsable

Samia
CHEHBI GAMOURA

Liste des intervenants

	Intervenant(s)	Email	Volume horaire
	Emer BUTLER	emer.butler2@em-strasbourg.eu	25 h CM

Discipline

Quantitative methods / Statistics

Descriptif

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.

MAC

Analyser et critiquer une vision stratégique et managériale dans un contexte incertain/Analyse and critically assess a s

10 ECTS

Objectifs pédagogiques - COGNITIVE DOMAIN

A l'issue du cours, l'étudiant(e) devrait être capable de / d'...

analyze the situation about data organization and processing in the organization
audit the data pipeline in the organization
examine the situation in the oprganization to detect the value to add by employing Big Data project embedding AI solution
evaluate the maturity level of an organization for Big Data implementation
organize and manipulate the business data collection and acquisition in data bases

Objectifs pédagogiques - AFFECTIVE DOMAIN

A l'issue du cours, l'étudiant(e) devrait être capable de / d'...

formulate a combination of data sources in an integrated Big Data System (example of MapReduce query)
prepare a study of a situation to justify the need a Big Data system in an organization
question the big data base to extract valuable information (example of BigQuery)
exemplify an integrated system of Big Data for an organization
revise the delivered work in groups on practical exercices and cases on AI

Objectifs pédagogiques — Objectifs de développement durable (ODD)

ODD n°9 - Industry, innovation and infrastructure

ODD portés par la politique RSO de l'EM.

Plan / Sommaire

Part 1: Big Data (BD)
1.1. Introduction to Data and Big Data Concepts
1.2. The Big Data Life Cycle
1.3. Data Processing Techniques (Hadoop, MapReduce)
1.4. Data Quality, Visualization, Compliance, and Preparation
1.5. Big Data Project Execution

Part 2: Big Data Analytics (BDA)
2.1. Overview of Big Data Analytics
2.2. Types of Analytics
2.2.1. Traditional Data Analytics
2.2.2. Advanced Analytics Approaches

Part 3: Artificial Intelligence (AI)
3.1. Introduction to Artificial Intelligence
3.2. AI for Business Analytics
3.3. Different Branches of AI
3.4. Machine Learning Techniques
3.4.1. Supervised Learning: Decision Trees in Warehousing
3.4.2. Unsupervised Learning: Association Rule Algorithms in Marketing
3.5. Generative AI

Part 4: Hands-on Practice with Google BigQuery & Python (or alternative tools)
4.1. Working with Tables and Datasets
4.2. Creating Queries and Extracting Data
4.3. Practical Examples of AI Coding and Programming

Part 5: Ethical and Social Considerations in Big Data and AI 5.1. Issues of Data Privacy and Security
5.2. Fairness and Bias in Algorithms
5.3. Trust, Accountability, and Transparency in AI
5.4. The Impact of AI on Jobs and the Workforce
5.5. Designing Ethical AI and Governance Principles

Part 6: The Future of Big Data and AI in Business
6.1. New and Emerging Trends in Big Data and AI
6.2. Automation and Autonomous Systems
6.3. Human-Centered Approaches to AI
6.4. Regulatory and Legal Challenges in AI and Big Data

Prérequis nécessaires

Connaissances en / Notions clés à maîtriser

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

Supports pédagogiques

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

Software

- Other : Python, BigQuery

Additional electronic platforms

- Other : Python, BigQuery

Manuels/ouvrages obligatoires

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’.

Adossement à la recherche

Bibliographie/références académiques

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.

Travaux de recherche de l'EM

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.

Modalités d'évaluation

Liste des modalités d'évaluation

? Evaluation intermédiaire / contrôle continu 1

Ecrite et orale (180 min) / En groupe / Anglais / Pondération : 10%

Compétence mesurée

Non renseignée

? Evaluation intermédiaire / contrôle continu 2

Ecrite (30 min) / Individuelle / Anglais / Pondération : 10%

Compétence mesurée

Non renseignée

? Evaluation intermédiaire / contrôle continu 3

Ecrite (40 min) / En groupe / Anglais / Pondération : 10%

Compétence mesurée

Non renseignée

✓ Evaluation finale 4

Ecrite (180 min) / Individuelle / Anglais / Pondération : 70%

Compétence mesurée

Non renseignée

✓ Seconde chance

Ecrite (180 min) / Individuelle / Anglais / Pondération : 70%

Compétence mesurée

Non renseignée