KdG University of Applied Sciences and Arts
Brusselstraat 45
2018 ANTWERPEN
BELGIUM
03 613 13 13
info@kdg.be
Data & A.I. 326382/2547/2425/1/49
Study guide

Data & A.I. 3

26382/2547/2425/1/49
Academic year 2024-25
Is found in:
  • Applied Computer Science (English Programme), programme stage 2
    Specialisation:
    • Artificial Intelligence
This is a single course unit.
Study load: 6 credits
Weight: 6,00
Total study time: 150,00 hours
This course unit is marked out of 20 (rounded to an integer).
Re-sit exam: is possible.
Possibility of deliberation: This course unit is eligible for deliberation according to the criteria as determined by the degree programme you are enrolled in.
Teaching staff: Magerman Tom, Tawalbeh Saja
Language course: No
Languages: English
Scheduled for: Term 1+2

Study materials - general information: Mandatory

All learning materials can be found on the LMS (Learning Management System) or will be provided by your lecturer.

Teaching methods - general information

This course unit is offered based on the principles Blended Learning. This means that a choice has been made to use a form of education in which digital study material is combined with face-to-face instruction. Face-to-face instruction is based on the preparation by the student.

Teaching methods

Total study time150,00 hours

Learning outcomes

CodeDescription
OLR AIT 1.1The BSc IT professional applies the appropriate analysis and design techniques, recognises patterns and translates a case into functional and non-functional requirements
OLR AIT 1.2The BSc IT professional devises tailor-made solutions for the customer, examines various alternatives and makes a reasoned choice
OLR AIT 2.1The BSc IT professional builds new systems using a robust architecture and applies relevant quality standards and insights.
OLR AIT 2.2The BSc IT professional tests systems or implementations and validates these with the customer
OLR AIT 2.4The BSc IT professional integrates existing and new systems into a coherent whole
OLR AIT 2.6The BSc IT professional collects and processes data and process metrics, stores them and makes them available to retrieve them correctly and efficiently
OLR AIT 3.1The BSc IT professional strives for the maintainability of systems and/or maintains systems

Learning objectives

You apply modelling techniques (a.o. normalisation) to design databases.
You apply advanced SQL techniques.
You apply performance optimizations to the database and in SQL queries
You use a Database and a Data Science development environment, and you modify them autonomously according to your needs and insights
You know, and you have an eye for the limitations of relational Databases and Data Science models and techniques
You identify the most suitable Data Science model, taking into account the available data, the intended purpose and the measurement levels.
You apply the Data Science model on the available and relevant data to achieve the wanted goal ( predictions, classifications, …).
You critically evaluate the achieved results from the Data Science model (a.o. by using evaluation metrics)

Subject matter

  • Logical and physical database design
  • Normalisation techniques
  • Query plan interpretation
  • Database optimisation techniques
  • Query optimisation techniques
  • Probability Rules of Sum and Product, Bayes Law, Law of total probability
  • Different kind of probability distributions (ex. normal, binomial)
  • Hypothesis testing with one population
  • Different A.I. techniques
  • Evaluation metrics
  • Linear discriminant analysis
  • Meta heuristics
  • Artificial neural networks
  • Working in and programing with a data science development environment (Python)


Entrance guidance and study counselling

This course integrates course specific guidance. For additional guidance, such as tutoring or mentoring, contact your Study Career counsellor to assess your specific needs.

Assessment - general information

For this course, we assess you based on of a written open book examen with laptop.

Details can be found on LMS (learning management system) or will be given by your lecturer.

Assessment

Evaluation(s) for first exam chance
MomentForm%Remark
During the exam period at the end of term 1Written open book exam with laptop30,00Term 1
During the exam period at the end of term 2Written open book exam with laptop70,00Term 1+2
Evaluation(s) for re-sit exam
MomentForm%Remark
Re-sit Exam PeriodWritten open book exam with laptop100,00Term 1+2

Description Course Sequence

You can add this course unit to your study programme if you obtained a credit or a condoned fail mark for:
- DATA & A.I. 1
- AND for DATA & A.I. 2