Teacher(s)
Language
English
Prerequisites
Basic knowledge in mathematics and statistics (including multivariate statistical analysis using supporting statistical software).
Learning outcomes
At the end of this learning unit, the student is able to : | |
The main objective of this course is to help students become reflective, responsible business leaders making sound data-driven decisions. This course accordingly helps bridge the gap between managers and data scientists. In particular, students – after taking this course – should be able to:
|
|
Content
In today’s economy, organizations have access to an unprecedented amount of data, including information on how consumers interact with brands and respond to marketing efforts, as well as data on how employees feel, behave, and respond to organizational initiatives. Data is even being referred to as the new “oil” of the economy. In addition, the rise of artificial intelligence (AI) is transforming business intelligence and analytics. However, effectively leveraging data and AI to support business decisions remains a major challenge for many organizations. This course explores the increasingly important role of data and AI in business and examines how organizations, as well as you as a future professional, can develop competitive advantages and create impact through decision-driven business analytics.
This course teaches widely-used frameworks, techniques and approaches of business analytics including descriptive analytics, experimental design, and predictive analytics (machine learning). Students implement them through simulation exercises and case studies to solve various business problems related to customer behavior, employee and customer churn prediction, digital ads optimization, and website traffic prediction, among others. Students will learn and use these techniques to leverage and turn data into relevant business insights. Particular attention is paid to corporate digital responsibility, that is the legal and ethical issues behind data gathering, storage and usage.
This course covers techniques and tools such as:
This course teaches widely-used frameworks, techniques and approaches of business analytics including descriptive analytics, experimental design, and predictive analytics (machine learning). Students implement them through simulation exercises and case studies to solve various business problems related to customer behavior, employee and customer churn prediction, digital ads optimization, and website traffic prediction, among others. Students will learn and use these techniques to leverage and turn data into relevant business insights. Particular attention is paid to corporate digital responsibility, that is the legal and ethical issues behind data gathering, storage and usage.
This course covers techniques and tools such as:
- data preparation with Excel Power Query
- data visualization and advanced reporting with the business intelligence tool Tableau
- statistical data analyses and machine learning with R
Teaching methods
The course includes theoretical lectures, case studies, hands-on exercises, a hackathon, and a workshop led by practitioners from top companies. This CEMS course is practice-oriented. Most concepts and analytical techniques discovered throughout this course will be taught through use-cases and how-to examples, using different business intelligence and statistical software.
The professor reserves the right to adapt the course format and eventually opt for a blended learning format that combines online and in-class activities.
Students are required to carry out preparatory work prior to some sessions, and to take an active role in class discussions
The professor reserves the right to adapt the course format and eventually opt for a blended learning format that combines online and in-class activities.
Students are required to carry out preparatory work prior to some sessions, and to take an active role in class discussions
Evaluation methods
Students' performance is fully assessed on a continuous basis. Grades will be based on the following activities:
- Take-home assignment on experimental design (team & individual) (35%)
- Machine learning hackathon (team) (15%)
- Accenture business intelligence project (team) (45%)
- Class participation (individual) (5%)
- This is a single-assessment course! The score obtained is final for the ongoing academic year; you cannot re-sit the aforementioned activities and improve your score.
- Grades attained through the team assignments and projects can be individualized according to each member’s investment.
- By submitting an assignment for evaluation, you assert that (i) it accurately reflects the facts, which means that you have verified them, especially if they originate from GenAI resources; (ii) all sources are suitably attributed; and (iii) you have respected all specific requirements of the assigned work, in particular requirements for transparency and documentation of process, or have explained where this was not possible. If any of these assertions are not true, whether by intent or negligence, you have violated your commitment to truth, and possibly other aspects of academic integrity. This constitutes academic misconduct.
Other information
This course is exclusively reserved for CEMS students of the LSM.
Bibliography
All course material is (or will be) available from the course website (see Moodle).
Recommended readings
Textbooks:
Recommended readings
Textbooks:
- Evans, J. (2020), “Business Analytics,” 3rd edition, Pearson Educations.
- Frank, M., Braginsky, M., Cachia, J., Coles, N., Hardwicke, T., Hawkins, R., Mathur, M., & Williams, R. (2025), “Experimentology—An Open Science Approach to Experimental Psychology Methods,” The MIT Press, Cambridge (UK).
- Provost, F., & Fawcett, T. (2013), “Data Science for Business – What You Need to Know About Data Mining and Data-Analytic Thinking,” O'Reilly Media Inc.
- Tan, P-N et al. (2021), “Introduction to Data Mining,” 2nd edition, Pearson Educations.
- Berman, R., & Israeli, A. (2022), “The Value of Descriptive Analytics: Evidence from Online Retailers,” Marketing Science, 41(6), 1074-1096.
- Bradlow, E., Gangwar, M., Kopalle, P., & Voleti, S. (2017), “The Role of Big Data and Predictive Analytics in Retailing,” Journal of Retailing, 93(1), 79-95.
- George, G., Osinga, E., Lavie, D., & Scott, B. (2016), “Big Data and Data Science Methods for Management Research,” Academy of Management Journal, 59(5), 1493-1507.
- Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., & Potapov, D. (2020), “Digital Analytics: Modeling for Insights and New Methods,” Journal of Interactive Marketing, 51, 26-43.
- Hickman, L., Huynh, C., Gass, J., Booth, B., Kuruzovich, J., & Tay, L. (2025), “Whither bias goes, I will go: An integrative, systematic review of algorithmic bias mitigation,” Journal of Applied Psychology, 110(7), 979–1000.
- Lobschat, L., Müller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M. and Wirtz, J. (2021), “Corporate digital responsibility,” Journal of Business Research, 122, 875-888.
- Van Auken, S. (2015), “From Consumer Panels to Big Data: An Overview on Marketing Data Development,” Journal of Marketing Analytics, 3(1), 38-45.
- Villarroel Ordenes, F. and Silipo, R. (2021), “Machine Learning for Marketing on the KNIME Hub: The Development of a Live Repository for Marketing Applications,” Journal of Business Research, 137, 393-410
- Wedel, M., and Kannan, P.K. (2016), “Marketing Analytics for Data-Rich Environments,” Journal of Marketing, 80(6), 97-121
Faculty or entity
