Data Science and AI, MSc
Chalmers University of Technology
Key Information
Campus location
Gothenburg, Sweden
Languages
English
Study format
On-Campus
Duration
2 years
Pace
Full time
Tuition fees
SEK 320,000
Application deadline
Request info
Earliest start date
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Introduction
Data Science and AI, MSc
The digital revolution has seen data science and AI become crucial elements of everyday life. Machine learning, and the technologies and methodologies for processing enormous amounts data are also creating a wealth of new opportunities. Consequently, skilled data scientists and AI engineers are in huge demand in all manner of situations. This programme will offer you a solid foundation in machine learning, resulting in a fantastically wide range of options after graduation.
Data science is a highly cross-disciplinary field, using data to gain deeper understanding and insight to support decision making. Applications are numerous, from the natural sciences and healthcare to business and finance. Relevant computational methods include algorithms for collecting and handling large-scale data, statistical methods such as Bayesian modelling, and machine learning techniques such as deep neural networks.
AI is concerned with designing and building intelligent systems. Recent advances have brought the field to the next level, and it is currently undergoing rapid changes. Machine learning techniques within AI enable computers to perform complex tasks they have not been explicitly programmed to do — successful examples of this include machine translation, computer vision, game playing and self-driving vehicles.
This programme educates engineers to undertake a wide variety of challenges in handling and analysing different kinds of data, using and developing software in complex data-intensive and AI-related applications. An excellent understanding of both theory and practice is required, including the possibilities and limitations of existing and evolving technologies, and how to apply these responsibly.
Admissions
Scholarships and Funding
Scholarships are a great source of funding for Master's students who are liable to pay tuition fees. Some of these are administrated by Chalmers and others by external institutions. Additional scholarships may be appended to the list and applicants are therefore encouraged to check this webpage regularly.
Please visit the university website for more information.
Curriculum
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
Program Outcome
Credits: 120
Program Tuition Fee
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 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