LLSMF2011 - 2024 GrB

Data mining, which is the main topic of this course is at the crossroad of marketing, IT and statistics. Because of the digitalization of the world, Marketing Information System (MIS) has evolved, and these 3 disciplines must now interact to be effective and serve best your business. The purpose is to give you, as future business owners, some keys to be able to interact with IT, data scientists and extract the best of all the resources of a company by understanding what is/is not possible, why, what can we change, …?


The material covered in the courses of bachelor in Business Engineering. In particular, students are assumed to be familiar with basic concepts of statistics and econometrics, financial accounting, managerial accounting, and mathematics for business. Knowledge of statistical and econometrics programming languages such as R-studio, and/or Matlab, etc, is assumed.

Main themes

We live in a complex environment, where the interconnections among economic agents (firms, consumers, ect.), their choices/decisions under uncertainty and as a response to unforeseen events determine the successfulness of firms’ activities. The last global economic crisis driven by the Covid-19 pandemic, the great financial crisis, the digital transformation, and the pressing need for a transition towards a greener economy, are just some examples how complex and uncertain the firms’ competitive arena can be. In this course, students will learn basic tools that companies can use to identify, report and analyze the risks/opportunities that a complex environment can bring to firms’ activities.

Upon completion of this course, students will:

  • Be able to understand and critically assess the risks an organization is exposed to;
  • Critically assess the reporting of risk in corporations and associated strategic reporting practices;
  • Analyze the risks a corporation is exposed to;
  • Apply empirical work in a (relatively) new software (R, Python, etc.).


As the scope of the course is broad, the team of instructors will select a range of topics based on their background, interests and experience. Potential covered topics are (but not limited to):
Part 1. Introduction:
  • What is Volatility, uncertainty, complexity and ambiguity (VUCA)? Sources & consequences
  • The corporate search for resilience  
  • Risk assessment for the board (from analysis of annual reports)
Part 2. Qualitative aspects:
  • Governance & annual reports
  • Risk awareness
  • Internal control
  • Risk identification
  • Risk management
  • Etc.
Part 3. Quantitative aspects:
  • Valuation techniques
  • Decision trees
  • Simulation techniques
  • Sensitivity and scenario analysis
  • Real option analysis
  • Stress testing
  • Etc.

The course will be centered around the following teaching methods:
  • In-class lectures
  • Practical sessions
  • Regular meetings with the professors and assistants
  •  Case studies
  • Guest lecture

Teaching methods

Prior to participation in those activities, students will be provided with learning material and compulsory readings that will be pivotal for the understanding of the teaching activities.


1) Business Risk Management: Models and Analysis; Edward J. AndersonISBN: 978-1-118-34946-5 December 2013 384 Pages, available at: https://www.amazon.com/Business-Risk-Management-Models-Analysis-ebook/dp/B00G7PLYAW/ref=tmm_kin_swatch_0?_encoding=UTF8&qid=&sr=

2) Business Case Analysis with R: Simulation Tutorials to Support Complex Business Decisions 1st ed. Edition, by Robert D. Brown III , available at: https://www.amazon.com/Business-Case-Analysis-Simulation-Tutorials/dp/1484234944