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There is a rapidly increasing demand for specialists who are able to exploit the new wealth of information in large and complex databases to improve analysis, prediction and decision making.
The programme focuses on modern developments in the intersection of statistics, machine learning, artificial intelligence and database management, providing students with a unique competence in the labour market.
With the growth of computer capabilities, databases become larger and more complex, making traditional statistical methods less effective or even unsuitable. Data from economic transactions, individual health records, internet search, and telecommunications are just a few examples of the content of enormous databases that challenge professional analysts. In these data-rich environments, methods from data mining, machine learning, statistical visualisation, computational statistics and other computer-intensive statistical methods included in the programme have become increasingly popular for 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 degree in one of these areas, or an engineering degree.
Students who have finished the bachelors programme Statistics and Data Analysis (Statistik och dataanalys) at Linköping University will find our masters programme to be the natural continuation of their studies where they can learn more about various data analysis and machine learning methods, including Bayesian methods, text mining and statistical methods in bioinformatics.
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 analysing shopping patterns in supermarkets' scanner databases
- how to build a spam filter
- how to provide an early signal of a financial crisis by analysing the frequency of crisis-related words in financial media and internet forums
- how to estimate the effect that a new legislation on traffic has on the number of deaths
- how to use a complex DNA micro array dataset to learn about the determinants of cancer
- how interactive and dynamic graphics can be used to determine the origin of an olive oil.
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 do exchange studies during the third semester. Our partner programmes were carefully selected in order to cover various methodological perspectives and applied areas.
During the final semester of their studies, students receive help in finding a private company or a governmental institution where they can write their theses. There they can apply their knowledge to a real problem and meet people who use advanced data analysis in practice.
Career opportunitiesThere is a rapidly increasing demand for specialists able to analyse large and complex systems and databases with the help of modern computer-intensive methods. Do you know for example that Barack Obamas administration was looking for Data Mining analysts for the elections in 2012?
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.
Compared to the bachelors degree of the same subject, the masters programme provides the opportunity to work with the development of methods and to search for senior positions or jobs with a more analytical profile.
Students aiming at a scientific career will also find the programme an ideal background for future research. Many of the programme's lecturers are internationally recognised researchers in the fields of statistics, data mining, machine learning, database methodology and computational statistics.
Degree: Master of Science with a major in statistics (120 credits)
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: clustering and association analysis, Computational Statistics and Bayesian learning constitute the core of the programme.
In addition, masters 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 masters thesis of 30 ECTS credits.
First year of studiesIntroductory courses
- Statistical methods, 6 credits
- Advanced R programming, 6 credits
Compulsory courses
- Introduction to Advanced Academic studies, 3 credits
- Introduction to Machine Learning, 9 credits
- Data Mining Clustering and Association Analysis, 15 credits
- Philosophy of Science, 3 credits
- Bayesian Learning, 6 credits
- Computational statistics, 6 credits
Profile courses
- Time series analysis, 6 credits
- Advanced Machine Learning, 6 credits
Complementary courses
- Web Programming and Interactivity, 4 credits
- Neural networks and learning systems, 6 credits
Profile courses
- Visualization, 6 credits
- Multivariate Statistical Methods, 6 credits
- Probability Theory, 6 credits
- Statistical evidence evaluation, 6 credits
Complementary courses
- Data Mining Project, 6 credits
- Text mining, 6 credits
- Optimization, 6 credits
- Database Technology, 6 credits
- Bioinformatics, 6 credits
Master's thesis, 30 credits
Want to improve your English level for admission?
Prepare for the program requirements with English Online by the British Council.
- ✔️ Flexible study schedule
- ✔️ Experienced teachers
- ✔️ Certificate upon completion
📘 Recommended for students with an IELTS level of 6.0 or below.
LiU International Scholarships
Linköping University offers scholarships to new students with excellent academic results. Scholarships within the programme LiU International Scholarships will result in a tuition fee waiver.
Who is eligible?
- Students who have applied for masters programmes at Linköping University in time (before 15 January)
- AND who have chosen a programme at Linköping University as the first priority (ranked as No.1 out of 4)
- AND who are required to pay tuition fees
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