Data Science

120 credits (MSc, 2 years )

With the digital revolution, Data Science and Artificial Intelligence (AI) has become an important part of our lives and in society as a whole. Vast amounts of data are available in science, industry and other organizations, ranging from molecular biology to social media, from quantum physics to business and retail. In addition, the quickly emerging technologies for processing large-scale data and machine learning is creating a wealth of opportunities, in analyzing such data for understanding and insight, and for building AI systems for new complex tasks, where automated decision making is becoming a reality. Skilled data scientists and AI engineers are in high demand everywhere. With a solid foundation in machine learning, and in understanding the diverse problems of Data Science and AI, you will have a wide range of career opportunities to choose from.

What is Data Science and AI?

Data Science is a highly cross-disciplinary field concerned with how to extract useful knowledge from data, for deeper understanding and decison support. It is based on a blend of methods in statistics and machine learning, together with computational techniques and algorithms for handling large-scale data. Examples of application areas include biology and other sciences, healthcare, business, finance and different kinds of internet data. Computational methods range from algorithms for collecting and handling large-scale data, statistical methods such as Bayesian modelling, to machine learning techniques such as deep neural networks.

AI is about building intelligent systems, and is currently a rapidly evolving field thanks to recent advances in machine learning with large-scale data, where machine learning enables the computer to perform complex tasks without being explicitly programmed. Successful examples of this approach include machine translation, computer vision, game playing and self-driving vehicles.

Through their use of large-scale data and machine learning, the fields of Data Science and AI are closely connected. They are also connected in their application since it is common to first collect and analyze data to better understand the problem, and then to build algorithms and systems for decision support and autonomous decision-making. Therefore, with an increased demand for advanced information systems and computer applications in a wide range of areas, Data Science and AI are becoming necessary ingredients in software development in general.

Programme aim

The overall aim of the programme is to educate engineers who can undertake a wide variety of challenges in handling and analyzing different kinds of data, and who are able to use and develop software in complex data-intensive and AI-related applications. This requires a good understanding of both theory and practice, including the possibilities and limitations of existing and evolving technologies, and how to responsibly apply these in various situations.

Programme description

The courses of the programme will provide a solid foundation in machine learning, statistics and optimization, with an in-depth understanding of the mathematical modelling techniques used for extracting information from large sets of complex data, and with the computational skills and algorithms for working with such data. You will also gain familiarity with a range of common problems within Data Science and AI which can be solved with such techniques.

The first year includes five compulsory courses: an introduction to Data Science and AI, Nonlinear optimization, Stochastic processes and Bayesian statistics, Applied machine learning, and Design of AI systems. This will give you an introduction and a good foundation for the field. The purely mathematical courses in statistics and optimization are important for data science and AI in several ways and form the mathematical foundations of machine learning. The applied courses will give you a good combination of applied theory and hands-on experiences. The courses will also include considerations of ethical, social and environmental issues.

The compulsory elements in the second year are a project course and the Master’s thesis. In addition, you will need to choose a number of compulsory elective courses and you can further choose from a wide range of elective courses (a detailed list of available courses will appear soon). At the end of the programme you will complete a 30 credit Master’s thesis project, where the acquired knowledge and skills are put into practice.

Through a combination of theory and practice in the courses of the program, you will gain an understanding of how and why certain models and algorithms work and be able to identify their possibilities and limitations. You will be able to approach a real-world problem in a specific problem domain, combining existing and new methods to create an efficient solution. You will be able to continuously learn in these rapidly evolving fields, communicate with experts and non-experts in specific problem domains and to responsibly apply these technologies. You will also gain the insights to be able to understand and influence the roles of Data Science and AI in society.

Why apply

The programme will lead to a wide range of career opportunities within many different application domains, e.g. virtually every other engineering discipline, as well as within medicine and finance. You will be well equipped to pursue a career in industry or government, as well as for further doctoral studies and an academic career.

Chalmers has renowned expertise within many of the Data Science and AI subareas, including machine learning, bioinformatics, image analysis and computer vision, natural language processing, databases, large-scale algorithms and optimization, stochastic modelling, Bayesian and spatial statistics. The Gothenburg area region includes many companies with extensive activities in these areas. There are also major initiatives in AI at Chalmers and nationally in Sweden, creating additional opportunities in the future.

Several departments besides the department of Computer Science and Engineering and the Department of Mathematical Sciences offer courses where data science and/or AI is applied to their specific subdomain, including the departments of Space, Earth and Environment, Physics, Biology and Biotechnology, Electrical engineering, Chemistry and Chemical engineering, Microtechnology and Nanoscience, and Technology Management and Economics.

Who should apply

The programme is a natural extension of computer science and mathematics/statistics studies but is also suitable if you come from a different technological background, with a sufficient basis in mathematics and computer science, and wish to broaden your perspectives with an in-depth introduction to Data Science and AI.​

Other Programmes that might interest you
Complex Adaptive Systems
Computer Science – Algorithms, Languages and Logic
Computer Systems and Networks
Engineering Mathematics and Computational Science
High-Performance Computer Systems
Software Engineering and Technology ​​​
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Course plan

Compulsory courses

  • Introduction to Data Science and Artificial Intelligence
  • Nonlinear optimization
  • Stochastic processes and Bayesian statistics
  • Applied Machine Learning
  • Design of AI systems
  • Data Science Project
  • Master’s thesis

Compulsory elective and elective courses

You will be able to choose from a number of courses in, among others,  the following areas:

  • Machine learning, Data science and AI
  • Statistics and stochastic processes
  • High Performance Computing
  • Bioinformatics
  • Optimization
  • Algorithms
  • Signal processing
A detailed list of compulsory elective and elective courses will appear soon.

Career opportunities

There is a huge demand for engineers with a solid foundation in Data Science and AI, and as the computational power and the amount of data available rapidly increases, the need will only continue to grow. Upon graduation you can pursue a wide range of career opportunities in software development and applying data science and AI in academia, industry and government.​

Published: Thu 25 Nov 2010. Modified: Fri 26 Oct 2018