EM054M8K

Programme
PGE
MGA
UE
Big Data Analytics
Semestre
B
Discipline
Supply chain management
Volume horaire
27 H
Nombre de places
40
Ouvert aux visitants
Oui
Langue
EN
Responsable
Samia CHEHBI GAMOURA

Liste des intervenants

Intervenant(s) Email Volume horaire CM
Samia CHEHBI GAMOURA samia.gamoura@em-strasbourg.eu 27 h

Contribution pédagogique du cours au programme

LEARNING GOAL 1 : Students will master state-of-the-art knowledge and tools in management fields in general, as well as in areas specific to the specialized field of management.
Students will identify a business organization’s operational and managerial challenges in a complex and evolving environment.
Students will understand state-of-the-art management concepts and tools and use them appropriately.
Students will implement appropriate methodologies to develop appropriate solutions for business issues.

Descriptif

The purpose of this course is to provide students with an overview of theory fundamentals and practical cases regarding the use of Big Data and Analytics in management and organizations.

Organisation pédagogique

Face-to-face

- Lectures
- Other : Demo

In group

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

Interaction

- Discussions/debates

Others

Aucun élément de cette liste n'a été coché.

Objectifs pédagogiques

Cognitive domain

A l'issue du cours, l'étudiant(e) devrait être capable de / d'...
  • - (niv. 1) Identify The paradigm of Big Data and Analytics, and the related concepts such as Data sources, Artificial Intelligence, Internet of Things, Machine Learning, Data Science, etc.,
  • - (niv. 2) Differentiate management cases where managers are able to apply Big Data Analytics in order to facilitate decision making in management,
  • - (niv. 3) Acquire The approaches of analytics that are adopted by the organizations,
  • - (niv. 5) Prepare and use the concepts and trends underlying current and future methods of Big Data Analytics in the management of organizations,
  • - (niv. 6) Evaluate , understand, control, and plan a Big Data Project in management with the use of Analytics.

Affective domain

A l'issue du cours, l'étudiant(e) devrait être capable de / d'...
Aucun affective domain n'a pour le moment été associé à ce cours.

Objectifs de développement durable abordés

Aucun objectif de développement durable n'a été coché.

Plan / Sommaire

1. Big Data 1.1. Data Concept 1.1.1. Data 1.1.2. Information 1.1.3. Knowledge 1.2. Big Data Concept 1.2.1. Volume 1.2.2. Variety 1.2.3. Velocity 1.2.4. Veracity 1.3. Big Data Life Cycle 1.3.1. Data Acquisition 1.3.1.1. Multi-Channels 1.3.1.2. Internet of Things 1.3.1.3. Cloud/Grid Computing 1.3.2. Data Storage 1.3.2.1. Data Base 1.3.2.2. Data Warehouse 1.3.2.3. Data Lake 1.3.2.4. Data Security 2. Analytics 2.1. Concept 2.2. Types 2.2.1. Traditional Analytics 2.2.1.1. Business Intelligence (BI) 2.2.1.2. OnLine Analytical Processing (OLAP) 2.2.2. Advanced Analytics 2.2.2.1. Artificial Intelligence 2.2.2.2. Data Mining 2.2.2.3. Machine Learning 3. Big Data Analytics 3.1. Concept 3.2. Role 3.3. As a field 3.4. Technically 3.5. Big Data Analytics Life Cycle 3.5.1. Data Processing 3.5.1.1. Hadoop 3.5.1.2. MapReduce 3.5.2. Data Quality 3.6. Big Data Analytics Project 3.7. Technological and Social Mutations 3.8. In Management 3.8.1. Applications 3.8.1.1. Predictive Management 3.8.1.2. Prediction in Unstructured BPM 3.8.1.3. Marketing/Sales Compliance 3.8.1.4. Supply Chain Advanced Risk Management 3.8.1.5. Supply Chain Predictive Risk Management

Prérequis nécessaires

Connaissances en / Notions clés à maîtriser

Required background in information systems and Management. In addition to notions in Data Bases

Supports pédagogiques

Mandatory tools for the course

- Computer
- Other : Online software

Documents in all formats


- Worksheets

Moodle platform

Aucun élément de cette liste n'a été coché.

Software

Aucun élément de cette liste n'a été coché.

Additional electronic platforms

Aucun élément de cette liste n'a été coché.

Bibliographie recommandée

Ouvrages principaux

Book: ‘Data Analytics Made Accessibly’. 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).

Littérature complémentaire

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 : Veillez à mobiliser au moins une ressource

Peuvent être renseignés les manuels coordonnés, les traductions de manuel, les études de cas traduites etc…
- The data warehouse toolkit : the definitive guide to dimensional modeling (PEGE Library - EM Strasbourg)

Modalités d'évaluation

Liste des modalités d'évaluation

Evaluation intermédiaire / contrôle continu 1Séance n° 4
Ecrite et orale (20 min) / en groupe / Anglais / pondération : 30 %
Précisions : Practical Part (Mini-Project & rewarding) (PCS): 30% Practical mini-project by groups. Homework of 2 weeks. Mini-project on a real-world case study based on a visit/contact/interview with an enterprise employing Big Data Analytics. Preparation and defense through a presentation in class. 15min duration for the presentation and 5min for questions/answers. Proceeding by vote for evaluation.
Cette évaluation sert à mesurer LO1.1, LO1.3
Evaluation finaleDernière séance
Ecrite (180 min) / individuelle / Anglais / pondération : 70 %
Précisions : Theoretical Part (Written Exam) (TWE): 70% Written exam with supervision in class. Duration of 180 minutes (last session). Theoretical and practical questions of real-world problems and case studies to resolve. Individual efforts.
Cette évaluation sert à mesurer LO1.2, LO1.3
Aucune modalité d'évaluation n'a pour le moment été attribuée à ce cours.