Statistics and Data Mining, M.Sc.

  • Application Deadline
  • 24 months
    Duration
  • Tuition
    19900
    Tuition (Full programme)
    Free
    Tuition (Full programme)
  • English (take IELTS)
    Language
There is a rapidly increasing demand for specialists who are able to exploit the new wealth of information in large and complex datasets to improve analysis, prediction and decision making. The Statistics and Data Mining programme at Linkping University focuses on modern developments at the intersection of statistics, artificial intelligence and database management.

About

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 Statistics and Data Mining 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 and text mining.

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. 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 also find the programme the 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.

Programme Structure

First year of studies

Introductory courses
  • Statistical methods
  • Advanced R programming
Compulsory courses
  • Introduction to Advanced Academic studies
  • Introduction to Machine Learning
  • Data Mining Clustering and Association Analysis
  • Philosophy of Science
  • Bayesian Learning
  • Computational statistics
Profile courses
  • Time series analysis
  • Advanced Machine Learning
Complementary courses
  • Web Programming and Interactivity
  • Neural networks and learning systems
Second year of studies
Profile courses
  • Visualization
  • Multivariate Statistical Methods
  • Probability Theory
  • Statistical evidence evaluation
Complementary courses
  • Data Mining Project
  • Text mining
  • Optimization
  • Database Technology

Detailed Programme Facts

  • Full-time duration 24 months
  • Study intensity Full-time
  • Credits
    120 ECTS
  • Languages
    • English
  • Delivery mode
    On Campus

English Language Requirements

You only need to take one of these language tests:

  • Minimum required score: 6.5

    The IELTS – or the International English Language Test System – tests your English-language abilities (writing, listening, speaking, and reading) on a scale of 1.00–9.00. The minimum IELTS score requirement refers to which Overall Band Score you received, which is your combined average score. Read more about IELTS.

    Take IELTS test
  • Minimum required score: 575

    The TOEFL – or Test OF English as a Foreign Language – offers a paper-based test (PBT). The final, overall PBT score ranges between 310 and 677, and is based on an average taken from the three test components (listening, structure, and reading). The writing part of this test is scored separately on a scale of 0-6. Read more about TOEFL (PBT).

  • Minimum required score: 90

    The TOEFL – or Test Of English as a Foreign Language – offers an internet-based test (iBT). The final, overall iBT score ranges between 0 and 120, and includes a scaled average from the four components (reading, listening, speaking, and writing). Read more about TOEFL (iBT).

Academic Requirements

A bachelors degree in one of the following subjects: statistics, mathematics, applied mathematics, computer sicence, engineering or a similar degree. Courses in calculus and linear algebra, statistics and programming 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 bachelors essay or project. If applicants hold a degree that does not include a bachelors essay or project, their Letter of Intent should describe previous studies and any academic activities that are related to the masters programme or the programmes applied for.

English B/English 6 or equivalent.

Tuition Fee Per Full Programme

  • EUR 19900 International
  • Free EU/EEA
  • 190000 kr

Funding

StudyPortals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.