This programme teaches methods for analysis, processing and transmission of large-scale data and signals, using tools from machine learning, signal processing, and information and communication theory. Choose among three specialisations: Images and Vision, Data Science, and Connectivity. Project work is offered in collaboration with tech companies.

Data Science and Information Engineering, Master's Programme

Autumn 2024 / Full-time / Linköping

Closed for late application

Data Science and Information Engineering, Master's Programme - Second admission round mainly for Swedish and EU/EEA students

Autumn 2024 / Full-time / Linköping

We are living in an increasingly networked society. The emergence of new applications such as augmented, virtual and extended reality, and the Internet of Things, are set to revolutionise the coming decades. The exponential growth in the capability of artificial intelligence and machine learning, and the proliferation of wireless communication devices, require skilled engineers to drive technological development and inspire new inventions. This programme provides tools to develop these new technologies. It is highly competitive – you must possess advanced skills in mathematics, programming, and a dedication to your education.

Complex networks and signal analysis

Mandatory courses include machine learning, complex networks and big data, multidimensional signal analysis, information and communications engineering, as well as distributed information processing and learning.

From the second semester you follow one of these specialisations:

  • Images and Vision: Courses mainly on image processing and computer vision.
  • Data Science: Advanced information processing and machine-learning knowledge for the analysis of and inference from big data.
  • Connectivity: Wireless systems and their fundamentals, with a range of courses on communications and information storage, compression, and transmission.

Compulsory project course and lab sessions

Teaching consists of lecture-based instruction and tutorials. Most courses include computer project lab sessions. The third semester has a compulsory project course during which you learn about project management, apply your knowledge to solve a larger problem, and work in teams with other students. The final semester is devoted to your thesis, which may be carried out either in collaboration with a tech company or as an internal project with the University. Ericsson, Saab, Sectra, and Qualcomm are among the major tech companies with a presence in Linköping.

Excellent research opportunities

Linköping University has world-class research in computer vision, data science, and the development of 6G – the next-generation cellular network technology. As a student, you will be fully immersed in this environment. We can help put you in touch with groups and companies in these fields.

Work as an engineer or continue with research

The programme prepares you for a career as engineer in industry. If you are interested in research, you will also be qualified for postgraduate studies towards a PhD degree.

Syllabus and course details

Here is a preliminary schedule for all four semesters (two years). Detailed information related to the first semester can be found in our Study Information database (big blue button below). For entry requirements and tuition fees, please click the ”Admission requirements” tab at the top of the page.

Semester 1 (compulsory courses)

Detection and estimation of signals, 6 credits
Information and Communications Engineering, 6 credits
Multidimensional Signal Analysis, 6 credits
Complex networks and big data, 6 credits
Machine Learning, 6 credits

Semester 2 (compulsory and elective courses)

Algorithmic Problem Solving, 6 credits
Embedded Perception Systems, 6 credits
Computer Vision for Video Analysis, 6 credits
Digital and wireless communications, 6 credits
Data Compression, 6 credits
Natural Language Processing, 6 credits
Data Mining - Clustering and Association Analysis, 6 credits
3D Computer Vision, 6 credits
Image and Audio Compression, 6 credits
Multiple Antenna Communications, 6 credits
Signal Processing for Communications, 6 credits
Sensor Fusion, 6 credits
Bayesian Learning, 6 credits

Semester 3 (compulsory and elective courses)

Images and Graphics, Project Course CDIO, 12 credits
Digital Image Processing, 6 credits
Machine Learning for Computer Vision, 6 credits
Project Course in Signal Processing, Communications and Networking, CDIO, 12 credits
Information Networks, 6 credits
Modern Channel Coding, Inference and Learning, 6 credits
Modelling and Learning for Dynamical Systems, 6 credits
Advanced Machine Learning, 6 credits
Distributed information processing and machine learning, 6 credits

Semester 4

Degree project - Master’s Thesis, 30 credits


Application and admission

Essential information

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