Program Overview

Program Overview

The Master's program in Data Science and Artificial Intelligence (DS&AI) is designed to educate students who can combine advanced data analytics techniques and AI methods to understand, apply, and create systems that behave intelligently and extend human intelligence in a responsible, transparent, and explainable way.

Program Objectives

The main aim of the program is to produce Masters of Science in engineering who are able to support and enhance 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 deep knowledge and understanding of Data Science methods with the state-of-the-art methods of AI.

Vision of AI Engineer

The 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.

Core Content

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.

Areas of Focus

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

Learning Outcomes

The program aims to produce graduates with the following learning outcomes:

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.

Curriculum

The curriculum includes:

• Curriculum start year 2024/2025 and before
• Program Trajectories - Expertise Areas
• Free electives
• SCOP/e - Study and Career Orientation Program
• Graduation
• Internal double diploma

Internships and Exchange

The program offers:

• Internship in a company

Student Guidance and Support

The program provides student guidance and support through various means.

Graduation

The graduation process involves:

• Administrative process
• Seminar
• Preparation phase
• Graduation project
• Graduation in a company
• Research clusters and groups
• Thesis writing guidelines

Examination Committee

The examination committee is responsible for:

• Submitting a request
• Submitting a study program
• Appeals to the Examination Appeals Board (CBE)
• Examination Schedules
• Final Examination Schedule

Rules and Regulations

The program is governed by rules and regulations, including:

• Academic Fraud
• Forms

Admission

The program has specific admission requirements.

Organization and Facilities

The program is organized and supported by:

• Online Systems
• Student assistantship
• Program Board
• Program Committee
• Study Association