Linköping University

Overview

The Master's programme in Statistics and Machine Learning at Linköping University equips students with advanced skills in statistical modelling, machine learning, and data analysis. Participants will learn to design, evaluate, and implement data-driven methods to address real-world challenges across various fields, including research and industry.

What you'll study

The programme spans four semesters, culminating in a master's thesis and a total of 120 ECTS credits. It integrates core components of advanced statistics, machine learning, and data-centric computing.

Core learning areas include:

• Statistical methods and computational statistics
• Machine learning and deep learning
• Bayesian learning and probabilistic modelling
• Data mining, text mining, and big data analytics
• Time series and multivariate statistical methods
• Programming and data handling (including Python and R)
• Data visualisation, web technologies, and decision-making foundations

Typical course components feature:

• Statistical methods
• Probability theory
• Decision theory
• Computational statistics
• Machine learning (including advanced topics)
• Neural networks and learning systems
• Bayesian learning
• Advanced data mining
• Big data analytics
• Time series analysis
• Multivariate statistical methods
• Text mining
• Database technology
• Data visualisation
• Web programming
• Introduction to machine learning and Python (as applicable)
• Advanced R programming
• Philosophy of science (as applicable)
• Data mining project (team-based work)
• Master’s thesis (capstone project)

Profile and electives:

In the latter stages of the programme, students can choose profile and complementary courses. Depending on availability, there may also be opportunities to participate in exchange studies.

Entry requirements

Applicants must possess a bachelor's degree (or equivalent) in statistics, mathematics, applied mathematics, computer science, engineering, or a closely related discipline. Additionally, candidates should have completed coursework with a passing grade in the following areas:

• Calculus
• Linear algebra
• Statistics
• Programming (at a level comparable to English upper secondary education requirements for English 6/B; exemptions for Swedish 3 may apply)

Note: The programme does not specify a GRE/GMAT requirement or a minimum GPA.

Career prospects

Graduates of the Statistics and Machine Learning programme are well-prepared for careers in a variety of sectors that require expertise in data analysis and machine learning. Potential roles include data scientist, statistician, machine learning engineer, and analyst, among others. The skills acquired during the programme are highly valued in both academic and industry settings, providing a solid foundation for further research or professional development.