CourseSpecifications
Valid as from the academic year 2024-2025

Predictive and Prescriptive Analytics (F000801)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
Bogaert, Matthias
Janssens, Bram

lecturegroup workseminar

EB23
EB23

Offered in the following programmes in 2024-2025
Master of Science in Business Engineering(main subject Data Analytics)
Master of Science in Business Engineering (Double Degree)(main subject Data Analytics)
Master of Science in Business Engineering (Double Degree)(main subject OperationsManagement)
Master of Science in Business Engineering(main subject Operations Management)
Exchange programme in Economics and Business Administrationlecturer-in-charge
co-lecturer
crdts
666offering
AAA

66AA

Teaching languages
EnglishKeywords
Advanced predictive models, advanced optimization and simulation methods, data science,machine learning, deep learning, reinforcement learning, ensembles, forecasting, Python
Position of the course
This course builds on the content of techniques seen in ‘Machine Learning’, ‘Analytics
Customer Relationship Management’ and ‘Social Media and Web Analytics’. Specific emphasis
will be put on business applications of analytics. The course will not only cover more advanced
methods in predictive analytics, but also prescriptive analytics. All examples and exercises will
be performed in Python.
Contents
The techniques covered in ‘Machine Learning’, ‘Analytics Customer Relationship Management’and ‘Social Media and Web Analytics’ are considered as basic knowledge in this course.The
first part of the course will primarily focus on the theory about the advanced predictive
analytics, prescriptive analytics, the combination of predictive and prescriptive analytics andreinforcement learning. In prescriptive analytics several mathematical optimization methods will
be discussed, with the focus business application. The main goal will be to use these
optimization techniques to make better business decisions. Finally, the course will cover
reinforcement learning, focusing on the trade-off between exploration and exploitation. The
techniques covered in reinforcement learning will focus on business application (e.g.,markdown management) and not on computer vision (e.g., self-driving cars).
• Introduction to predictive and prescriptive analytics• Advanced predictive analytics• • Feature selection• • Ensemble methods (e.g., XGBoost, lightGBM, and catboost)• • Heterogeneous ensemble• Prescriptive analytics with exact optimization• Linear and integer programming(Approved)1• Prescriptive analytics with meta-heuristics• • Genetic algorithms• • Particle swarm optimization• • Simulated annealing• • Gradient descent• • Multi-objective optimization• Forecasting• • Statistical forecasting• • Machine learning forecasting• • Forecasting with deep learning• Reinforcement learning• • Exploration vs exploitation• • Multi-armed bandit problem• • Markov decision process• • Q-learning
The second part of the course will be devoted to several business applications. The businessapplication will be discussed with a focus on identifying and solving data-related problems.
Depending on the application, new analytical methods can be introduced. The emphasis will
always be put on the application and how data analytics can add value.
• Topics:• • HR analytics• • Fraud analytics• • Data science and ethics (including explainable artificial intelligence)• • Value-driven analytics• • Advanced NLP for businessInitial competences
The students should have profound knowledge of the algorithms discussed in ‘Machine
Learning’, ‘Analytical Customer Relationship Management’ and ‘Social Media and Web
Analytics’. The student should have knowledge of Python.
Final competences
1 Understanding predictive, prescriptive and reinforcement learning methods.
2 Using Python to apply these methods
3 Correctly interpreting, reporting, and presenting the discussed methods.
4 Recognizing and solving a specific data-driven business-related problem from start to finish.
5 Have insight into the several business applications and know the data-related problems in
1 these specific applications.
6 Knowing and understanding when to use and when not to use a specific method in a certain
1 context.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Group work, Seminar, Lecture
Extra information on the teaching methods
Ex cathedra sessions as well as active class discussions of the different techniques andmodels with interactive exercises in the PC room.
Study material
Type: Slides
Name: Slides theory
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Available on Ufora : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No(Approved)

2

Type: Software
Name: Code files in Python
Indicative price: Free or paid by faculty
Optional: no
Available on Athena : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No
Usability and Lifetime within the Course Unit : regularly
Usability and Lifetime within the Study Programme : regularly
Usability and Lifetime after the Study Programme : regularly
References
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. "O'Reilly Media, Inc.".
Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud analytics using descriptive,predictive, and social network techniques: a guide to data science for fraud detection. JohnWiley & Sons.
Verbeke, W., Baesens, B., & Bravo, C. (2017). Profit driven business analytics: A practitioner'sguide to transforming big data into added value. John Wiley & Sons.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
Course content-related study coaching
For the course content-related the students will receive coaching the lecturer and the teachingassistant. During the practical exercises students will be coached by the lecturer and theteaching assistant. There will be several feedback moments (during lectures, group andindividual meetings).
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment
Examination methods in case of periodic assessment during the second examination period
Written assessment
Examination methods in case of permanent assessment
Presentation, Peer and/or self assessment, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is not possible
Extra information on the examination methods
Periodic: written exam with open and multiple choice questions (closed book)
Permanent: project work in group with written report, presentation corrected with peer
assessment.
Calculation of the examination mark
Periodic: 50%
Permanent: 50%
When the student obtains less than 10/20 for the periodic evaluation, he/she can no longer
pass the course unit as a whole. If the total score does turn out to be a mark of 10 or more outof 20, this is reduced to the highest fail mark (9/20).

(Approved)3