Course structure and curriculum
Structure and curriculum
The DS&AI course is a two-year master's program. Both years are divided into semesters that run from September to January and from February to July. There are two quartiles of eight weeks in each semester in which you take courses. Your knowledge is tested in examination periods of two to three weeks.
Students follow a core program and specialization courses, special electives, and professional development modules. There is room for an internship or an international exchange in your free elective space. When you return to the TU/e, you start your final graduation phase.
Free elective space
Graduation phase/professional development
You start building your program by following mandatory core courses to lay the foundation of your degree. The DS&AI master is organized in seven course trajectories. Each of them represents an expertise area of Data Science and Artificial Intelligence.
DS&AI specialization tracks
Data Science and AI in Context
As an engineer working in data science and artificial intelligence, you will encounter societal, ethical, and domain-specific issues. The courses in this trajectory expose you to these issues and teach you how to address them when designing new solutions.
Data Management and Engineering
Data Management Systems (DMS) provide fundamental, underlying infrastructures to identify and extract value from data for organizations and society. The courses in this trajectory teach you about the foundations, applications, and engineering of next-generation data and knowledge management methods and systems.
Algorithmic Data Analysis
Algorithms play an undeniable role in data science: they enable efficient and automated data handling, analysis, and visualization. In this trajectory, you will learn how to develop algorithmic solutions for high-quality data analyses and for making optimal recommendations in a verifiable and explainable manner. A broad set of algorithmic tools is an essential part of data scientist's repertoire.
Explainable Data Analytics
The actual analysis or (prediction) problem you need to solve is typically unknown at the start of a project, while the solution requires a valid and explainable integration of domain knowledge, data, and models. The courses in this trajectory equip you with the mindset, foundational knowledge, and engineering skills to achieve this with two unique specializations available only at TU/e: Visual Analytics and Process Mining.
The statistics track provides rigorous methods and models for summarizing data of various phenomena into understandable features that have a direct impact on and enable the interpretation of real-world situations. The courses in this trajectory teach you a broad range of statistical methods to analyze and model temporal and big data sets, and how statistical methods can be used to learn from this type of data.
Data Mining and Machine Learning
Within Data Mining and Machine Learning, you study the foundations and practical approaches of knowledge extraction from vast collections of complex data. This trajectory focuses on data mining and machine learning approaches and techniques to develop extremely prevalent, end-to-end solutions for algorithmic decision making. You probably already use them multiple times a day without even knowing it!
AI and Machine Learning
This track involves the study of algorithms that improve through experience. The courses involved teach you the main techniques and approaches in modern AI, with an important focus on solutions that are not only accurate but, most of all, efficient, reliable, interpretative, robust, and trustworthy.
In your specialization electives, you opt for at least two of these expertise areas, taking several courses in each. You then broaden your specialization by selecting courses from one or two other DS&AI trajectories that become your 'minor'. These courses help prepare you for your graduation phase.
The program ends with a research project in which you prove yourself as an engineer in Data Science and Artificial Intelligence. During the project, you use what you have learned and the skills you have developed to create new knowledge and designs. You will specialize in a single subject and demonstrate that you are able to organize a research project independently.
For a more in-depth breakdown of the course curriculum, click here.