Statistics and Data Mining

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 bachelor’s programme Statistics and Data Analysis (Statistik och dataanalys) at Linköping University will find our master’s 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 opportunities

There 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 Obama’s 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 bachelor’s degree of the same subject, the master’s 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, 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.

First year of studies

Introductory 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

Second year of studies

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

Programme specific requirementsA bachelor’s degree with at least 90 credits, i.e. 18 months of full-time study, in mathematics, applied mathematics, statistics or computer science. The undergraduate courses in mathematics should include both calculus and linear algebra. Basic undergraduate courses in statistics and computer science are also required.Each applicant must enclose a letter of intent written in English, explaining why they want to study this programme, and a summary of their bachelor's essay or project. If applicants hold a degree that does not include a bachelor's essay or project, then their letter of intent should describe previous studies and any other academic activities related to the master's programme.All supporting documents, including the letter of intent and the specific paper, should be sent to University Admissions in Sweden, FE 1, SE–838 73 Frösön, Sweden. English Language Requirements IELTS band: 6.5 TOEFL paper-based test score : 575 TOEFL iBT® test: 90

Scholarships

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 master’s 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