Data is the driving force behind today's information-based society. There is a rapidly increasing demand for specialists who are able to exploit the new wealth of information in large and complex systems.
The programme focuses on modern methods from machine learning and database management that use the power of statistics to build efficient models, make reliable predictions and optimal decisions. The programme provides students with unique skills that are among the most valued on the labor market.
The rapid development of information technologies has led to the overwhelming of society with enormous volumes of information generated by large or complex systems. Applications in IT, telecommunications, business, robotics, economics, medicine, and many other fields generate information volumes that challenge professional analysts. Models and algorithms from machine learning, data mining, statistical visualization, computational statistics and other computer-intensive statistical methods included in the programme are designed to learn from these complex information volumes. These tools are often used to increase the efficiency and productivity of large and complex systems and also to make them smarter and more autonomous. This naturally makes these tools increasingly popular with both governmental agencies and the private sector.
The programme is designed for students who have basic knowledge of mathematics, applied mathematics, statistics and computer science and have a bachelor’s degree in one of these areas, or an engineering degree.
Most of the courses included in the programme provide students with a deep theoretical knowledge and practical experience from massive amounts of laboratory work.
Students will be given the opportunity to learn:
- how to use classification methods to improve a mobile phone’s speech recognition software ability to distinguish vowels in a noisy environment
- how to improve directed marketing by analyzing shopping patterns in supermarkets’ scanner databases
- how to build a spam filter
- how to provide early warning of a financial crisis by analyzing the frequency of crisis-related words in financial media and internet forums
- how to estimate the effect that new traffic legislation will have on the number of deaths in road accidents
- how to use a complex DNA microarray dataset to learn about the determinants of cancer
- how interactive and dynamic graphics can be used to determine the origin of an olive oil sample.
The programme contains a wide variety of courses that students may choose from. Students willing to complement their studies with courses given at other universities have the possibility to participate in exchange studies during the third term. Our partner programmes were carefully selected in order to cover various methodological perspectives and applied areas.
During the final term of the programme, students receive help in finding a private company or a government institution where they can work towards their thesis. There they can apply their knowledge to a real problem and meet people who use advanced data analytics in practice.
Syllabus and course details
The programme runs over two years and encompasses 120 credits, including a thesis.
The introductory block of courses contains a course in basic statistics offered for students with a background in computer science or engineering, and a course in programming offered for students having a degree in statistics or mathematics. The courses Introduction to Machine learning, Data Mining, Big Data Analytics, Computational Statistics and Bayesian learning constitute the core of the programme.
In addition, master’s students have the freedom to choose among profile courses - aimed to strengthen students’ statistical and analytical competence - and complementary courses - that allow students to focus on particular applied areas or relevant courses from other disciplines. Opportunities for exchange studies are provided during the third semester of the programme.
To be awarded the degree, students must have passed 90 ECTS credits of courses including 42 ECTS credits of the compulsory courses, a minimum of 6 ECTS credits of the introductory courses, a minimum of 12 ECTS credits of the profile courses, and, possibly, some amount of complementary courses. The students must also have successfully defended a master’s thesis of 30 ECTS credits.
- Statistical methods, 6 credits
- Advanced R programming, 6 credits
- Advanced Academic studies, 3 credits
- Introduction to Machine Learning, 9 credits
- Advanced Data Mining, 6 credits
- Big Data Analytics, 6 credits
- Introduction to Python, 3 credits
- Philosophy of Science, 3 credits
- Bayesian Learning, 6 credits
- Computational statistics, 6 credits
- Time series analysis, 6 credits
- Multivariate Statistical Methods, 6 credits
- Web Programming, 6 credits
- Neural networks and learning systems, 6 credits
- Visualization, 6 credits
- Advanced Machine Learning, 6 credits
- Probability Theory, 6 credits
- Decision Theory, 6 credits
- Data Mining Project, 6 credits
- Text mining, 6 credits
- Database Technology, 6 credits
Master's thesis, 30 credits
Demand is increasing rapidly for specialists able to analyze large and complex systems and databases with the help of modern computer-intensive methods. Business, telecommunications, IT and medicine are just a few examples of areas where our students are in high demand and find advanced analytical positions after graduation.
Students aiming at a scientific career will find the programme the ideal background for future research. Many of the programme's lecturers are internationally recognized researchers in the fields of statistics, data mining, machine learning, database methodology and computational statistics.
Bachelor's degree equivalent to a Swedish Kandidatexamen within statistics, mathematics, applied mathematics, computer science, engineering or a similar degree. Courses in calculus and linear algebra, statistics and programming are also required.
English corresponding to the level of English in Swedish upper secondary education (English 6/B).
Selection will be based on:
- Academic merits and Letter of Intent
Each applicant should, therefore, enclose a letter of intent written in English, explaining why the applicant wants to study at the programme, how the applicant’s academic background is related to the contents of the programme and how the applicant’s academic background matches to the specific program requirements. If there are courses in the applicant’s transcripts that match to the courses mentioned in the specific requirements, the applicant is recommended to name these courses in the letter of intent. It is also recommended that the applicant includes a description of other relevant experience into the letter of intent (job experience, project participation etc. related to the programme’s specific requirements or to the programme contents).
190,000kr - NB: Applies only to students from outside the EU, EEA, and Switzerland.
Program taught in: