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,
• Statistics,
• 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

Preamble:  

“Data Science and Artificial Intelligence (DS&AI)” comprises advanced theories, algorithms, and methods from contemporary areas of DS&AI for collecting data and creating models, and for processing, managing, evaluating data and models and their relation for understanding, using, and developing engineering solutions that can support and enhance the human intellect. 

“Contemporary areas of DS&AI” are Data Engineering and Management, Algorithmic Data Analysis, Statistics, Visual Analytics and Process Mining, Data Mining and Machine Learning, and Artificial Intelligence and Machine Learning.  

1. Knowledge and understanding in DS&AI:  

• Graduates have a broad view of Data Science and Artificial Intelligence, and their interplay. 
• Graduates have in-depth knowledge and understanding of two or more contemporary areas of DS&AI and their theoretical and technical properties, assumptions, and limitations.
• Graduates are familiar with ethical and societal issues associated with the development and application of DS&AI.

2. Applying knowledge and understanding in DS&AI:  

• Graduates can analyze and translate complex data-rich and data-poor real-world problems into problem formulations that can potentially be solved using DS&AI. 
• Graduates can use DS&AI to design and create adequate engineering solutions to formulated problems that can be applied in real-world problem contexts. 
• Graduates can apply sound qualitative and quantitative methods for evaluating engineering solutions designed with DS&AI in real-world problem contexts. 

3. Making judgements and proficiencies in research and design in DS&AI:  

• Graduates can make reliable decisions with and critically reflect on engineering solutions using DS&AI in relation to real-world problems and data. 
• Graduates can critically reflect on the societal and ethical implications of solutions using DS&AI and their application to real-world problems. 
• Graduates can contribute new knowledge to DS&AI and its application through research or design. 

4. Communication:  

• Graduates can effectively communicate the relevance, methodology, results, and limitations and ethical aspects of engineering solutions using DS&AI (both orally and in writing) to scientific, specialist, and non-specialist audiences. 

5. Learning skills and attitude:  

• Graduates are independent, motivated, and self-actualized self-learners. 
• ​​​​​​​Graduates can identify gaps in their knowledge in relation to developments and the state-of-the-art of the field and take steps to close these. 

Want to know more? Contact the Academic Advisor DS&AI via the contact form below.