 | | |
| INFO2048-1 | Business Analytics
|
|
| Duration : | 30h Th |
|
| Number of credits : |
| Master in Management Sciences, in-depth approach, 1st year | | 5 |
|
| Master degree in Management, didactic approach, 1st year | | 5 |
|
| Master degree in Management, professional focus in Banking and Asset Management, 1st year | | 5 |
|
| Master degree in Business Engineering, professional focus in Performance Management and Control, 1st year | | 5 |
|
| Master en sciences de gestion à finalité spécialisée en digital marketing and sales management, 1st year | | 5 |
|
| Master degree in Business Engineering, professional focus in Financial Engineering, 1st year | | 5 |
|
| Master degree in Management, professional focus in Entrepreneurship, 1st year | | 5 |
|
| Master degree in Management, professional focus in Financial Analysis and Audit, 1st year | | 5 |
|
| Master in Management Sciences, professional Focus, 1st year | | 5 |
|
| Master degree in Management, professional focus in Human Management and Organization, 1st year | | 5 |
|
| Master in Management Engineering, professional Focus, 1st year | | 5 |
|
| Master degree in Business Engineering, professional focus in Intrapreneurship, 1st year | | 5 |
|
| Master degree in Management, professional focus in Management, 1st year | | 5 |
|
| Master en sciences de gestion, à finalité spécialisée en marketing and strategic intelligence, 1st year | | 5 |
|
| Master degree in Business Engineering, professional focus in Modelisation and Technologies, 1st year | | 5 |
|
| Master degree in Management, professional focus in Social Entreprise Management, 1st year | | 5 |
|
| Master degree in Management, professional focus in Strategic Intelligence and Marketing, 1st year | | 5 |
|
| Master degree in Business Engineering, professional focus in Supply Chain Management, 1st year | | 5 |
|
| Master degree in Business Engineering, professional focusin Performance Management Systems, 1st year | | 5 |
|
|
|
| Lecturer : | Michael Schyns |
|
Language(s) of instruction :
|
| English language |
|
Organisation and examination :
|
| Teaching in the second semester |
|
Course contents :
|
| No company can survive without a good management information system. Nowadays, a company must be able to collect, analyze and handle huge volume of data in order to answer managerial questions and/or offer new top value services. Amazon, Google, Facebook are obvious successful stories confirming the importance of data management.
Three keywords to define the course:
- Management field: Decision Making
- Approach: Data analysis (Analytics)
- Theory (basics) and applications
In this course, the main question is: How to transform raw "stupid" data into valuable "actionable" information? Common applications in marketing and sales are the analysis of customer loyalty, the analysis of the market basket, the identification of prospective customers)... Common applications in finance are fraud detection, credit risk analysis, risk profile, pricing... Common applications in eCommerce are recommendation tools, converting clicks into customers... Other common applications in Supply Chain Management are revenue management (e.g. airline ticketing), capacity management, diagnosis of production faults, forecasting demand, predictive modeling, advanced reporting based on ERP systems...
This course covers techniques starting from advanced reporting with Excel, reporting with advanced tools like Crystal Dashboard Design up to data mining tools like decision trees, neural networks or cluster analysis. |
|
Learning outcomes of the course :
|
|
- Gaining the knowledge and understanding of the chosen concentration field.
- Understanding and being able of using modelization methods.
- Capacity to research autonomously and methodically the information needed to solve a complex, transversal management problem.
- Integrate autonomously researched information, tools, knowledge and context to build and propose original, creative and viable solutions to concrete complex management problems whether real or simulated.
- Providing concrete solutions to a management problem, integrating modelization methods and/or a dimension of technology, innovation or production.
- Developing a critical sense (arguing).
- Professional capacity for written communication.
|
|
Prerequisites and co-requisites/ Recommended optional programme components :
|
| Basic computer skills |
|
Planned learning activities and teaching methods :
|
| Theory is immediately illustrated by small examples. Students perform exercises on computer during the lectures. Real cases are considered (e.g. revenue management, bankruptcy detection, customer choice...)
They will acquire a basic theoretical knowledge but also learn to use professional tools like Excel for basic reporting, SAP Cristal Dashboard for advanced reporting, R (CRAN) or Statistica for advanced business analytics (datamining). |
|
Mode of delivery (face-to-face ; distance-learning) :
|
| All lectures are given in a computer room. |
|
Recommended or required readings :
|
| Required:
PDF syllabus on Lol@
Recommended:
Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson Inertnational. |
|
Assessment methods and criteria :
|
| Exam in two parts
- Theory
- Practice on computer
|
|
Work placement(s) :
|
| |
|
Organizational remarks :
|
| |
|
Contacts :
|
| M. Schyns, HEC-ULG, N1
M.Schyns@ulg.ac.be |
|
|
| |
