CourseSpecifications
Valid as from the academic year 2024-2025

Big Data (F000802)

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 1)
EnglishGent

Lecturers in academic year 2024-2025
Van den Poel, Dirk

lectureseminargroup work

EB23

Offered in the following programmes in 2024-2025
Master of Science in Teaching in Economics(main subject Business Economics)
Master of Science in Business Economics (main subject Accountancy)
Master of Science in Business Economics (Double Degree)(main subject Accountancy)
Master of Science in Business Economics (Double Degree)(main subject CorporateFinance)
Master of Science in Business Economics (main subject Corporate Finance )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 Finance)
Master of Science in Business Engineering(main subject Finance)
Master of Science in Business Economics (Double Degree)(main subject Marketing)Master of Science in Business Economics (main subject Marketing)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
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Teaching languages
EnglishKeywords
Information Systems, Data Warehousing, SQL, Big Data, Apache Spark, Spark SQL, Python,PySpark, ML pipelines (Machine Learning).
Position of the course
The global objective of this course is to provide students with thorough theoretical as well aspractical knowledge on the use and management of information. This knowledge can be of astrategic, a technical-analytical, as well as an organizational nature.
Contents
1 Importance of information management in general: which developments are at the basis of
1 the increased importance of information use in marketing, which businesses or functions are
1 marketing information intensive?
2 Data sources and data collection methods: which data sources are available to
1 today's/tomorrow's data administrator, what is big data, how to deal with automized data
1 collection methods such as scanning and internet? How to handle the nosql evolutions?
3 Building a marketing database: which principles are at the basis of building a good marketing
1 database? How to build the structure (Entity Relationship Diagrams)?
4 Querying marketing databases: SQL (structured query language) programming language (in(Approved)11 casu: Oracle SQL and Apache Spark/Spark SQL/Python) with exercises on large existing
1 marketing information systems.
5 Implementation/integration of MIS in the organisation: which traps are related to the process
1 of implementing a MIS in the organisation, what are the principles of datawarehousing?
Each of these topics will be treated in-depth based upon a mixture of interactive classdiscussions, real-life cases.
In the big-data case NLP (natural language processing) and visualization will play a major role.
Initial competences
The "Data Mining" course.
Final competences
1 Understanding the structure and set-up of a database.
2 Mastering the programming language SQL (and NoSQL Big Data) to achieve optimal data
1 and information management.
3 Building applications based on these data structures in order to make adequate conclusions
1 for complex marketing problems.
4 Providing correct communication towards both technical and non-technical professionals.
5 Using a variety of external data sources (including new forms such as social media data) in
1 an optimal way.
6 Applying Big Data algorithms through Spark ML Pipelines on case studies in order to build
1 complex marketing decision models.
7 Validating own research results with existing literature of international top journals.
8 Executing a company case study in an international and interdisciplinary team that includes
1 different levels of experience.
9 Presenting professionally about an advanced problem and its solution.
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 and papers
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Available on Ufora : Yes
References
none
Course content-related study coaching
Numerous exercises are being solved during sessions. In addition, assignments (to be solvedin teams) are handed out. Students will receive coaching in the process of solving theassignments and feedback afterwards (collectively, by team and individually). After a test about
the programming language SQL, students will receive collective feedback.
Assessment moments
continuous assessment
Examination methods in case of periodic assessment during the first examination period
Examination methods in case of periodic assessment during the second examination period
Examination methods in case of permanent assessment
Oral assessment, Peer and/or self assessment, Written assessment, Assignment
Possibilities of retake in case of permanent assessment(Approved)2examination during the second examination period is possible
Extra information on the examination methods
Written examination to investigate the extent to which students mastered the use of entitiyrelationship diagrams, SQL query language. Team work using Apache Spark and the conceptof Big Data.
The use of generative AI is not allowed during the programming exam.
Calculation of the examination mark30% on SQL exercises/exam and 70% on Big Data/Apache Spark/Spark SQL/Python groupassignment potentially adjusted by peer assessment. Of the 70% group assignment score 30%
is based on individual questions and 40% is based on group performance.
To pass, a student should pass both parts of the evaluation. If a student does not pass for bothparts and the score is 10/20 or more, the score will be reduced to 9/20.

(Approved)3