With a specialisation in Data Engineering, you gain a broad knowledge in the field - from the cleansing, quality assurance and structuring of raw data, through infrastructure, to data-based knowledge generation.
The specialisation takes you through:
- Principles and technologies for data storage, data handling and accessing data in various database systems and on other platforms
- Models and techniques for obtaining and integrating data across domains, platforms and technologies
- Effective infrastructures and technologies for handling large amounts of data
- Analysing, reporting and visualising information from data
- Methods and techniques for finding patterns and knowledge in data (Machine Learning)
- Artificial Intelligence methods and techniques (e.g., Natural Language Processing (NLP), Image Recognition, Deep Learning, etc.)
The Data Engineering specialisation gives you the opportunity to choose electives according to your interests. Choice of electives include:
BUI – Business Intelligence
The Business Intelligence course teaches you how to work with realistic data using professional tools in Business Intelligence, such as Microsoft SSIS, SSRS, SSAS and PowerBi.
MAL – Introduction to Machine Learning
The Introduction to Machine Learning elective gives you knowledge and hands-on experience in using machine learning methods and tools for both structured and unstructured data problems. You will learn how to prepare data (through processing) and use it to determine underlying structures as well as making predictions.
NSQ – No-SQL versus relational databases
This elective gives you knowledge of the strengths and weaknesses of two fundamentally different approaches to database management systems (DBMS). The purpose of the subject is to support matching system requirements and DBMS technologies rather than a one-size-fits-all approach.
DLA – Deep learning for artificial intelligence
Here, we explore the fundamental concepts, techniques, and applications of deep learning in the context of artificial intelligence (AI). This provides you with a comprehensive understanding of how deep learning methods can be leveraged to solve complex AI problems.
DAI - Data Analysis Infrastructure
You will be introduced to various tools and techniques for data acquisition, cleansing, quality assurance and integration as well as to data modelling for analytics and basic visualization.
ERP – ERP Systems SAP ABAP/4 programming
This course introduces the fundamental aspects of analysis, design, coding and testing of programs in the SAP ABAP environment, and you will have the opportunity to work further with a selected part of the SAP system.
SMP - Stochastic modelling and processes
The course provides a solid introduction to the theory of stochastic processes. Special attention is given to applications and the student will model and analyse complex stochastic situations as encountered in practice. The applications include examples from various engineering fields such as information technologies and communications, signal processing, and more.