Data science and AI, MSc

120 credits (2 years)

Sign up for informationWith the digital revolution, Data science and Artificial Intelligence (AI) has become an important part of our lives and society as a whole. In addition, the quickly emerging technologies for processing large-scale data and machine learning is creating a wealth of opportunities 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, you will have a wide range of career opportunities.


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.

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 w​ith 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.

Through a combination of theory and practice in the courses of the programme, 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.

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, leading to a Master of Science (MSc) degree. 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 of 7.5 hp each that form a common foundation in Data science and AI:

  • Introduction to data science and artificial intelligence
  • Nonlinear optimization
  • Stochastic processes and bayesian statistics
  • Design of AI systems
These 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.​

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:

  • Algorithms
  • Options and mathematics
  • Applied Machine Learning*
  • Algorithms for machine learning and inference*
  • Computational techniques for large-scale data
  • Statistical learning for big data
  • Advanced databases
Group 2:
  • Causality and causal inference
  • Image processing**
  • High-performance computing
  • Machine learning for natural language processing
  • Basic stochastic processes and financial applications
  • Advanced probabilistic machine learning
  • Large scale optimization
  • Image analysis**
  • Advanced topics in machine learning
  • Spatial statistics and image analysis
  • Financial time series
*Only one of the courses between Applied 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

  • Algorithms
  • Advanced topics in machine learning
  • Advanced databases
  • Computational techniques for large-scale data
  • Algorithms for machine learning and inference
  • Causality and causal inference
  • Machine learning for natural language processing
  • Advanced probabilistic machine learning

Profile track: Large scale statistics

  • ​​Algorithms
  • ​Applied machine learning
  • ​Computational techniques for large-scale data
  • Advanced databases​
  • ​Statistical learning for big data
  • High-performance computing
  • Large scale optimization​
  • Causality and causal inference
  • Advanced probabilistic machine learning

Profile track: Image analysis and computer vision, alternative 1

  • Algorithms
  • Applied machine learning
  • Computational techniques for large-scale data
  • Statistical learning for big data
  • Spatial statistics and image analysis
  • Image processing
  • Statistical image analysis

Profile track: Image analysis and computer vision, alternative 2

  • Algorithms
  • Applied machine learning
  • Computational techniques for large-scale data
  • Statistical learning for big data
  • Spatial statistics and image analysis
  • Statistical image analysis
  • Causality and causal inference
  • Image analysis

Profile track: Financial data science

  • Algorithms
  • Options and mathematics
  • Applied Machine Learning
  • Statistical learning for big data
  • Advanced databases
  • Financial time series
  • Basic stochastic processes and financial applications
  • Causality and causal inference

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: 
  • Algorithms, advanced course
  • Linear statistical models
  • Computational methods in bioinformatics
  • Applied signal processing
  • Distributed systems
  • Empirical software engineering​
  • Linear integer optimization with applications
  • Bayesian statistics
  • Health Informatics
  • Deep machine learning
  • Artificial neural networks
  • Strategic management of technological innovation
  • Creating technology-based ventures

​Career

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 increase, the need will only continue to grow. 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.

Any organization that works with the analysis of data, and/or the development of computational tools, either as their actual end product or as means for further improvement of the internal work, require both data scientists and AI engineers. Such processes are often iterative, and both data science and AI engineering skills are needed in each step:
  • Data management: gathering, cleaning, transforming and storing data
  • Data analysis: identify trends, patterns and relationships in large data sets.
  • Tool development: use, develop and improve intelligent computer algorithms and tools to be robust, flexible and scalable
  • Machine learning: train and test tools and applications on relevant, clean data
  • Communication: interpret, visualize and communicate important findings from the data analysis
  • Decision making: support and improve the decision making process

Research within Data science and AI

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.
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Sustainable development​

The manufacturing industry is accountable for a significant portion of the global energy consumption, and the demand is only rising. Reports show a huge potential for data science and artificial intelligence to improve environmental sustainability and support the development of more efficient and eco-friendly production processes.

The programme is highly interlinked with the achievement of the UN Sustainable Development goals (SDGs). The table below provides an overview of the sustainable development goals and the associated targets within the programme.

SDGs for Data science and AI



Goal 3: Good health and well-being
The well-being and general health of the population are affected by a wide variety of factors, ranging from a clean and healthy environment, an efficient distribution of resources, and a solid and inclusive economy, to efficient and reliable healthcare. Data science and AI are key components in all these areas and the student learns how to build, improve and use robust, flexible and energy-efficient data science and AI tools to support these processes.
Goal 9: Industry, innovation and infrastructure
The programme is closely linked to industry through a continuous dialogue about the needed skill sets of the students and collaborative industry projects and internships. This both turns the focus towards the ability to build robust and sustainable infrastructures, as well as fostering innovation in the students. Moreover, the study plan includes several opportunities for deepening the knowledge in technological innovation and strategic management. 
Goal 11: Sustainable cities and communities 
By promoting skills for data analysis, predicting expected outcomes and supporting decision making, the logistics of manufacturing and the distribution of resources can be streamlined, and the use of toxic materials and excessive production of toxic waste can be reduced significantly. The students of the programme leave with the skill set for creating smarter, more efficient, and more sustainable developmental and manufacturing processes.

Student interview

“We use machine learning models to draw insights
Apoorva, India, Data science and AI


Why did you choose this programme?
– I did my bachelors in Computer science and engineering and during my final year, I found that my interest lies in the field of Artificial intelligence. I wanted a programme that had a nice balance between AI and other advanced technologies which used AI and covered both theoretical and practical knowledge in those fields. The programme structure of the master's in Data science and AI at Chalmers checked all those boxes for me.


What have you been working on?
– I am studying courses both in Data science and AI areas. During my third study period, we had a course where we designed AI systems and built machine translation models, recommendation systems, dialogue systems, game playing systems and so on which was very fun and interesting. I am currently taking a course that focuses on applying statistical methods/models to data and using machine learning models to draw insights, something I think is very essential to learn for everyone who wants to work in the area of Data science and AI. It all comes down to understanding the data before using it in the model. 

What do you like the most about your programme?
– The programme is structured in a way where we can apply the theoretical knowledge learnt to assignments which gives us a better understanding and "hands-on"-experience in these areas. Also, it covers most of the different subfields of AI and its applications in different areas like medicine, automobiles etc. enabling us to explore our interests in these areas. 

What do you want to do in the future?
–  After graduating I would like to apply the knowledge gained during my master's in the tech industry to build models to make better use of data. I would like to work as a Data scientist, Data engineer or AI engineer and put my knowledge to the best use.

​​Student Blogs

Page manager Published: Mon 05 Dec 2022.