Data Science & Artificial Intelligence
Vision of AI Engineer
As the name of the program implies, the DS&AI (MSc Data Science & Artificial Intelligence) program is grounded in two scientific disciplines: data science and artificial intelligence. The main aim of the program is to educate Masters of Science in engineering who are able to combine advanced data analytics techniques and AI methods in order to understand, apply and create systems that behave intelligently and extend human intelligence in a responsible, transparent, and explainable way.
We strongly believe that society needs experts who can support and enhance our human capabilities to solve complex problems, gain deeper understanding, and achieve results that were not attainable before in a trustworthy and explainable way, by analyzing (large amounts of complex) data and representing, analyzing, and reasoning over (domain)knowledge using the structured skills, techniques, and the deep knowledge and understanding of Data Science methods with the state-of-the-art methods of AI.
The DS&AI program has the ambition that DS&AI graduates are Data Scientists and AI Engineers with the ethos of a “civil engineer”, having deep technical abilities in the above expertise areas to develop smart solutions (instead of brute forcing) that
- are robust, trustworthy, fair, and secure,
- work together with people (not instead of),
- include the human factor in the process and in the result, and
- turn data into value under technical, social, and ethical aspects.
Hence the core content of the intended DS&AI program is the combination of its two underlying scientific disciplines, data science and artificial intelligence together with ethics and challenge-based learning. Data Science studies all principles and techniques of collecting, storing, managing, preparing, processing, analyzing, and visualizing data. Artificial Intelligence studies all principles and techniques for supporting and augmenting intelligent behavior. These two disciplines create knowledge from data and intelligence from knowledge.
There are many different areas within Data Science & Artificial Intelligence that support this process. The Master’s program DS&AI is organized around six areas, each containing three to four coherent courses within the program. These areas are:
- Data Engineering and management
- Algorithmic Data Analysis
- Explainable Data Analytics
- Data Mining and Machine Learning
- AI and Machine Learning
Through ethics and challenge-based learning, students integrate their skills from the different expertise areas in various real-life contexts, providing reflection on their methodology and way of working as an AI Engineer.
1.2 Learning Outcomes
All Master’s programs in the TU/e Graduate School comply with the intended learning outcomes (ILOs), a to j below, ILO b. is tailored to the specific domain of each master, and specified in additional learning outcomes.
Graduates from the DS&AI Master’s program:
a. are academically qualified to master’s degree level within the domain of ‘science engineering & technology’,
b. are competent in the relevant domain-specific discipline(s) at the scientific Master’s degree level, namely Data Science & Artificial Intelligence,
c. are able to conduct research and design independently,
d. have the ability and attitude to include other disciplines in their research, where necessary,
e. have a scientific approach to complex problems and ideas,
f. possess intellectual skills that enable them to reflect critically, reason and form opinions,
g. have the ability to communicate the results of their learning, thinking and decision-making processes at an international level,
h. are aware of the temporal and social context of science and technology (comprehension and analysis) and can integrate this context in their scientific work,
i. in addition to a recognizable domain-specific profile, possess a sufficiently broad basis to be able to work or collaborate in an interdisciplinary and multidisciplinary context. In this context, multidisciplinary means being focused on other relevant disciplines needed to solve the design or research problem in question,
j. have the ability and attitude to seek new potential applications, taking the social context into consideration.
Competence in DS&AI as mentioned in b. is specified in the following outcomes:
- have knowledge of methods and techniques from four or more areas of contemporary Data Science & Artificial Intelligence*,
- deeply understand and can apply methods and techniques from two or more different areas of contemporary Data Science & Artificial Intelligence and can contribute to extending these through research and design,
- are able to draw meaningful conclusions from data and context through the design and implementation of data science and artificial intelligence techniques, effectively turning data into value,
- understand the technical, ethical, and social aspects of data collection, and of solutions using data science and artificial intelligence techniques.
*) Contemporary areas of DS&AI are: AI and Machine Learning, Data Mining and Machine Learning, Data Engineering and Management, Algorithmic Data Analysis, Explainable Data Analytics, and Statistics.
Want to know more? Contact the Academic Advisor DS&AI via the contact form below.