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STAT-S406

Data management and business analytics

academic year
2024-2025
2025-2026

Course teacher(s)

Martine GEORGE (Coordinator)

ECTS credits

5

Language(s) of instruction

english

Course content

The course will present the fundamental principles of data management in business as well as some technical aspects. In particular, the course will cover the following topics:

  • Data analytics thinking
  • Relational database
  • SQL
  • Big data, small data
  • Data privacy
  • From business problem to data mining solutions
  • Data mining process
  • Predictive analytics
  • Evaluation and performance
  • Visualizing and telling a story with data
  • Data warehouse and business intelligence
  • Descriptive analytics
  • Analytical advantage
  • Text mining
  • Application of data analysis in different business contexts (functional, sectoral and of different company sizes)

This course contributes to the Sustainable Business Operations pedagogical pathway of the Sustainable Development initiative that seek to provide an integrated training in sustainability.
 

Objectives (and/or specific learning outcomes)

At the end of the course, the student will be able to:

  • Clarify a business issue by asking relevant questions.
  • Conduct analyzes of business issues based on facts and data.
  • Establish a strategy as to the nature of the data to be collected to respond to a business problem.
  • Understand and use the vocabulary specific to the analysis of business data.
  • Understand, articulate and coordinate the tools specific to each discipline involved (technical, quantitative analysis, business management, communication, project management).
  • Justify the analytical choices made

These skills will enable the future graduate, in particular, to be a qualitative stakeholder in the analytical value chain and to contribute to the company's transition towards data-oriented management.

Prerequisites and Corequisites

Required and Corequired knowledge and skills

MATH-S-201 Mathématique : fonction à plusieurs variables - 5 ECTS & STAT-S-202 Probabilité, inférences statistiques et
recherche opérationnelle - 10 ECTS

or any equivalent for students who have pursued their bachelor in another institution/university. For example:
ECGEB251 Mathématiques pour l’économie et la gestion II (Unamur) - 4 ECT

Teaching methods and learning activities

The diversity of learning activities will allow students to develop complex skills:

  • Conferences. Introduction to the theoretical concepts of data management.
  • Thematic workshops. Some concepts will be put into practice during specific one-hour sessions. Students will prepare these sessions based on theoretical concepts, written instructions and data. The workshop will be devoted to group work and discussions with the teacher.
  • Guest speakers who will present specific topics related to data management. Students must have integrated the messages and content presented and be able to use them in new issues.
  • Tutorials. The first tutorials will be assigned to data management. Each session will include a face-to-face session (to review concepts and answer student questions) and pre-session e-learning preparation (to practice coding). The final tutorials will focus on data-driven case studies and data mining processes. During each session, time will be allocated to the review of concepts and time will be allocated to case studies involving all the steps of the data analysis process according to the CRISP methodology.

References, bibliography, and recommended reading

  • Access to a coding learning platform.
  • A list of bibliographical references is provided before each session.

Contribution to the teaching profile

The course contributes to the following objectives of the Master in Management Sciences program:

LO 1.1. Integrate sustainable development in problem analysis

LO 1.2. Master and apply key economic and management concepts, frameworks and theories in a professional context to identify a business opportunity and build a relevant innovative solution to it

LO 1.3. Approach managerial or business problem through different disciplinary frameworks (law, communication, psychology, etc.) and taking external factors into consideration

LO 2.1. Adopt a scientific approach to data collection, research and analysis and communicate results with clear, structured and sophisticated arguments.

LO 2.2. Display critical thinking and develop a life-long learning approach

LO 3.1. Apply quantitative and qualitative techniques to support analysis using data with standard office and statistical software

LO 4.1. Work and communicate effectively as part of a team in an international and multicultural environment

LO 4.2. Demonstrate work ethics to foster corporate socially responsible conduct in the workplace

Other information

Contacts

Teacher: Martine George (martine.george@ulb.be)

Assistants: assistants in charge of exercises will carry out the practical work.

Campus

Solbosch

Evaluation

Method(s) of evaluation

  • Other

Other

The assessment is made on the basis of:

  • A group assignment. The assignment consists of responding to a business problem using data and analyzing a case as a whole by carrying out all the stages of the CRISP methodology. There will be 2 deliverables for this work, a written report and an oral presentation per group.
  • A final exam that will cover both knowledge and reasoning questions (in the form of MCQs) and reflection questions (in the form of open-ended questions). The objective is the understanding and the critical and concrete application of the concepts.

Mark calculation method (including weighting of intermediary marks)

The final grade will be composed this year:

  • The result of the group work (40%)
  • The result of the written exam (60%)

Language(s) of evaluation

  • english

Programmes