Data science and AI, MSc

120 credits (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 analysing 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.

Data science and AI master's programme at Chalmers

Data science is a highly cross-disciplinary field concerned with how to extract useful knowledge from data, for deeper understanding and decision 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 analyse 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.

The overall aim of the programme is to educate engineers who can undertake a wide variety of challenges in handling and analysing 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 four 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 only compulsory element in the second year is 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. 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 will 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 master's 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. 

Topics covered

The subjects of artificial intelligence, design of AI systems and stochastic processes are fundamental areas in the data science and AI master’s programme. The courses included in the programme plan handle topics such as machine learning, databases and algorithms.

Master's programme structure

The master's programme runs for a duration of two years. During each year, students can earn 60 credits (ECTS) and complete the programme by accumulating a total of 120 credits. Credits are earned by completing courses where each course is usually 7.5 credits. The programme consists of compulsory courses, compulsory elective courses and elective courses. 

Compulsory courses year 1

During the first year the programme starts with four compulsory courses that form a common foundation in Data science and AI. Each course is usually 7.5 credits. 
  • Introduction to data science and artificial intelligence
  • Nonlinear optimisation
  • Stochastic processes and bayesian statistics
  • Design of AI systems

Compulsory courses year 2 

In the second year you must complete a master's thesis worth 30 credits in order to graduate. 
  • ​Master’s thesis ​
Compulsory elective courses
You need to select at least two courses from the general group 1 and at least two from the profile specific group 2. You can then specialize on one of the profile tracks.

Group 1:

  • Databases
  • Applied machine learning*
  • Algorithms for machine learning and inference*
  • Techniques for large-scale data
  • Statistical learning for big data
  • Algorithms
Group 2:
  • Advanced machine learning
  • Statistical image analysis
  • Financial time series
  • High performance computing
  • Image processing**
  • Natural language processing
  • Large scale optimization
  • Basic stochastic processes and financial applications
  • Image analysis**
*Only one of the courses between Applies machine learning and Algorithms for machine learning and inference can simultaneously be counted in the exam.
** Only one of the courses between Image processing and Image analysis can simultaneously be counted in the exam.​

Profile track: Algorithms and machine learning

  • Databases 
  • Algorithms for machine learning and inference
  • Techniques for large scale data
  • Algorithms
  • Natural language processing

Profile track: Large scale statistics

  • ​Databases 
  • Applied machine learning
  • Techniques for large scale data
  • Statistical learning for big data
  • Algorithms
  • High performance computing
  • Large scale optimization

Profile track: Image analysis and computer vision, alternative 1

  • Databases
  • Applied machine learning
  • Statistical learning for big data
  • Statistical image analysis
  • Algorithms
  • Image processing

Profile track: Image analysis and computer vision, alternative 2

  • Databases
  • Applied machine learning
  • Statistical learning for big data
  • Statistical image analysis
  • Algorithms
  • Image analysis

Profile track: Financial data science

  • Databases
  • Applied machine learning
  • Statistical learning for big data
  • Financial time series
  • Algorithms
  • Basic stochastic processes and financial applications

Elective courses

You will also be able to select courses outside of your programme plan. These are called elective courses. You can choose from a wide range of elective courses, including the following: 
  • Computational methods for Bayesian statistics
  • Deep machine learning
  • Artificial neural networks
  • Numerical linear algebra
  • Stochastic optimization algorithms
  • Algorithms, advanced course
  • Empirical software engineering
  • Computer vision
  • Linear statistical models
  • Applied signal processing
  • Computational methods in bioinformatics
  • Distributed systems 1
  • Introduction to artificial intelligence
  • Discrete optimization
  • Distributed systems 2
  • Information vizualisation
  • Information theory for complex systems
  • Geographical information systems
  • Linear and integeroptimization with applications
  • eHealth
  • Bayesian statistics

Other Master's programmes that might interest you
Complex adaptive systems, MSc
Computer science – algorithms, languages and logic, MSc
Computer systems and networks, MSc
Engineering mathematics and computational science, MSc
High-performance computer systems, MSc
Software engineering and technology, MSc ​​​
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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.​

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Page manager Published: Mon 25 Jan 2021.