CourseSpecifications
Valid as from the academic year 2024-2025

Data Mining (F000759)

Course size
Credits 5.0(nominal values; actual values may depend on programme)
Study time 150 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 1)
EnglishGent

Lecturers in academic year 2024-2025
Benoit, Dries

lectureindependent work
seminar

EB23

Offered in the following programmes in 2024-2025
Bachelor of Science in Business Engineering
Exchange programme in Economics and Business Administration
Preparatory Course Master of Science in Business Engineering

lecturer-in-charge
crdts555offeringAAA

Teaching languages
EnglishKeywords
Univariate and multivariate regression analysis, classification, resampling methods, model
selection and regularization, tree-based methods, unsupervised learning, R
Position of the course
Business processes have been digitalized at high pace in recent decades. This lead to atremendous increase in information stored in databases. With datamining, analysts try to findrelevant patterns in this huge source of information that help management in optimizing theirdecisions. Students will learn how to extract relevant information from databases. Students willlearn how to apply a broad set of methods in a correct way to solve real-life business problems.
They will also learn the skills of how to interpret the results from such analyses in a meaningfulway. All exercises will be solved using the statistical programming language R.
This course builds on the concepts introduced in the course "Statistics I".
Contents* Relationship between bias and variance and model complexity* Regression analysis• simple linear regression• multiple regression• qualitative predictors* Classification• logistic regression analysis• linear discriminant analysis• k-nearest neighbors* Resampling methods• crossvalidation• bootstrap* Model selection and regularization• variable selection• shrinkage methods• dimension reduction* Unsupervised learning• principal component analysis• clustering(Approved)

1Initial competences
Students need to have a thorough understanding of the basic statistical principles and concepts
as treated in the course "Statistics I".
Final competences
1 Apply datamining methods in a correct way, using the programming language R
2 Interpret the results in a correct way and communicate these to a non-technical audience
3 Choose a well-suited method to tackle a specific business problem, knowing what the1 advantages and disadvantages are of the chosen approach.
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
Seminar, Lecture, Independent work
Extra information on the teaching methods
The learning management system Ufora will be used to coordinate the different diidactical
approaches used in this course.
Active participation in lab sessions via dodona-platform is required.
Study material
Type: Handbook
Name: An Introduction to Statistical Learning: with Applications in R (2nd edition)
Indicative price: Free or paid by faculty
Optional: noLanguage : English
ISBN : 978-1-07161-417-4
Online Available : Yes
Available in the Library : Yes
Available through Student Association : Yes
Usability and Lifetime within the Course Unit : intensive
Usability and Lifetime within the Study Programme : regularly
Additional information: Free (and legal) pdf can be downloaded from: https://www.statlearning.com/
References
James, G., Witten, D., Hastie, T. & Tibshirani, R. (2021) An Introduction to Statistical Learningwith Applications in R, Springer (Version 2). (Note: a free, legal version of the book can befound online).
Course content-related study coachingStudents can rely on the teaching assistants and teacher of the course. Communications andinteraction through the Ufora platform (group discussions, exercises, …). Example examquestions will be made available.
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
Participation, Written assessment open-bookPossibilities of retake in case of permanent assessment
examination during the second examination period is not possible
Extra information on the examination methods
Periodic evaluation (written exam):• Multiple choice questions• Open questions
Non-periodic evaluation (assignment):(Approved)2• Dodona exercises
Calculation of the examination mark95% periodic evaluation (written exam)
5% non-periodic evalutation (assignment)
Consequences of not respecting deadlines: Not respecting deadlines of the individual work and/ or group work without a well-motivated and substantiated reason (such as illness) leads to azero for individual work and/or group work, respectively.

(Approved)3