|Course set (UE) / Credits (ECTS) / Track / Specialization||Module :Big Data Analytics : 5 ECTS.|
|Discipline||Supply Chain Management|
|Open for visitors||yes (5 ECTS)|
|Working language :||English|
|Volume of contact hours :||27 h|
|Workload to be expected by the student :||108 h|
Track : Attendance
|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.|
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.
- 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.,
- Differentiate management cases where managers are able to apply Big Data Analytics in order to facilitate decision making in management,
- Acquire The approaches of analytics that are adopted by the organizations,
- Prepare and use the concepts and trends underlying current and future methods of Big Data Analytics in the management of organizations,
- Evaluate , understand, control, and plan a Big Data Project in management with the use of Analytics.
1. Big Data
1.1. Data Concept
1.2. Big Data Concept
1.3. Big Data Life Cycle
1.3.1. Data Acquisition
18.104.22.168. Internet of Things
22.214.171.124. Cloud/Grid Computing
1.3.2. Data Storage
126.96.36.199. Data Base
188.8.131.52. Data Warehouse
184.108.40.206. Data Lake
220.127.116.11. Data Security
2.2.1. Traditional Analytics
18.104.22.168. Business Intelligence (BI)
22.214.171.124. OnLine Analytical Processing (OLAP)
2.2.2. Advanced Analytics
126.96.36.199. Artificial Intelligence
188.8.131.52. Data Mining
184.108.40.206. Machine Learning
3. Big Data Analytics
3.3. As a field
3.5. Big Data Analytics Life Cycle
3.5.1. Data Processing
3.5.2. Data Quality
3.6. Big Data Analytics Project
3.7. Technological and Social Mutations
3.8. In Management
220.127.116.11. Predictive Management
18.104.22.168. Prediction in Unstructured BPM
22.214.171.124. Marketing/Sales Compliance
126.96.36.199. Supply Chain Advanced Risk Management
188.8.131.52. Supply Chain Predictive Risk Management
Required background in information systems and Management. In addition to notions in Data Bases
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).
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.
- The data warehouse toolkit : the definitive guide to dimensional modeling (PEGE Library - EM Strasbourg)
Behaviors such as
may lead to expulsion from the class/course.