CourseSpecifications
Valid as from the academic year 2024-2025

Big Data Technology (E018240)

Course size
Credits 4.0(nominal values; actual values may depend on programme)
Study time 120 h

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

Lecturers in academic year 2024-2025
De Witte, Dieter
Mannens, Erik

practicallecture

TW06
TW06

Offered in the following programmes in 2024-2025
Bridging Programme Master of Science in Bioinformatics(main subject Engineering)
Master of Science in Bioinformatics(main subject Engineering)
Master of Science in Industrial Engineering and Operations Research(main subject
Manufacturing and Supply Chain Engineering)
Master of Science in Industrial Engineering and Operations Research(main subject
Sustainable Mobility Analytics)
Master of Science in Industrial Engineering and Operations Research(main subject
Transport and Mobility Engineering)
Master of Science in Computer Science Engineering
Master of Science in Computer Science Engineering
Master of Science in Industrial Engineering and Operations Research

20.0h
20.0h

lecturer-in-charge
co-lecturer
crdts
444offering
AA
A

4A

4A

444A
AA

Teaching languages
EnglishKeywords
Big Data platforms & architecture, interactive data visualizations, knowledge graphs, FAIRdata, Open Data, stream management, information retrieval, recommender systems.
Position of the course
The main purpose of this course is to let the students gain hands-on experience with the mostimportant concepts of End-to-End Big Data Engineering. They will learn how to manage andvisualize Big Data.
Contents• Data collection & Open Data
• • Web scraping• • Data formats• Big Data Systems• • Spark & Hadoop• • MapReduce programming model• • Cloud AI & pre-trained models• Knowledge Graphs & FAIR• • FAIR data principles• • Linked Data
• • RDF databases• • Data spaces• • GDPR basics• Data Visualization 101• • Human Perception• • Design principles(Approved)1• • Interaction• • Data journalism• Data processing architectures• • Stream management system (messenging, processing)• • Building blocks and principles of Big Data architectures• • Lambda & Kappa architecture• • Modern designs: Microservices, Data Mesh and Data Virtualization• Information retrieval• • Inverted indexing• • Query matching• • Link Analysis• • Image Retrieval• • Impact of LLMs on retrieval• Recommender system• • Neighborhood-based• • Latent factor model• • Evaluation• Guest Lectures from Belgian Big Data companies
Initial competences• basic programming skills• • Experience with Python (passed the course Informatics E015041 or an equivalent course)• • Experience with Object Oriented Programming (passed the course Computer• • Programming E017210 or an equivalent course)• elementary understanding about basic data formats (CSV, TSV, etc.)• linear algebra• introductory course on statistics
Final competences
1 Understanding the possibilities and limitations of Big Data technology
2 Understanding the components of Big Data systems
3 Understanding the industry applications of Big Data
4 Combining Big Data components into a system architecture to meet specific product needs
5 Understanding the Big Data life cycle
6 Cleaning Big Data for production use
7 Visually and non-visually exploring Big Data
8 Creating interactive dashboards over Big Data
9 Handle datasets with multiple challenging dimensions (size, format, quality, …)
10 Dealing with high-velocity data via messaging and stream processing
11 Overcoming data heterogeneity through semantic technologies
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
Lecture, PracticalStudy material
Type: Handbook
Name: Mining of Massive Datasets (3rd edition)
Indicative price: Free or paid by faculty
Optional: no
Language : English
Author : Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
ISBN : 978-1-13905-845-2
Number of Pages : 315
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 : one-time(Approved)

2

Usability and Lifetime after the Study Programme : notReferences
Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey Ullman, ISBN: 978-1107-07723-2
Course content-related study coaching
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Oral assessment
Examination methods in case of periodic assessment during the second examination period
Oral assessment
Examination methods in case of permanent assessment
Oral assessment, Skills test, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods• Periodical evaluation• • Oral exam consists of 2 parts:• • • part I: Q&A with lecturer, with short preparation time (open book and open internet)• • • part II: open question where the student demonstrates his/her knowledge of the course• • • on a new problem (open book and open internet, large time window to record answer)• Non-periodical evaluation• • graded lab session reports in groups• • graded project reports, oral defense, and pitch deck.
• • examination during the second examination period is possible in modified form. The weight• • of the assignment will correspond to the workload for all labs and the project ~ 72 hours.
Calculation of the examination markThe student needs a 7/20 score for the permanent evaluation (labs + project) as well as for theexam.
If the student obtains less than 7/20 for one of the parts (permanent evaluation versus exam),the student can no longer obtain a pass mark for the course as a whole, in which case the final
mark will be capped at 7/20.
The weights for calculating the examination mark are as follows: 40% oral exam, 20% project,
40% labs.

(Approved)3