MSc Statistics and Machine Learning, 120 credits

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. 

Statistics and Data Mining, Master´s Programme

Autumn 2017, Full time 100%, Linköping, Campus Valla

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Statistics and Machine Learning, Master´s Programme

Autumn 2018, Full time 100%, Linköping

Apply

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 labour 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 visualisation, 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 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 analysing shopping patterns in supermarkets’ scanner databases
  • how to build a spam filter
  • how to provide early warning of a financial crisis by analysing 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.

Course details

Year 1

Introductory courses

Compulsory courses

Profile courses

Complementary courses

Year 2

Profile courses

Complementary courses

Master's thesis, 30 credits

Career opportunities

Demand is increasing rapidly 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 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.

Stories from the programme

Portrait photograph of international student ambassador Yumeng Li.

Yumeng Li

Second year student blogger

Hi! I am Yumeng with a multi-cultural background of more than 20 years living in Shanghai, China and four years working in Japan Airlines. I learn new things quickly and am happy to face challenges. Now I am studying the international master program of Statistics and Data Mining* in Linkoping University which is both challenging and interesting. (Statistics and Machine Learning from August 2018*)

Read Yumeng's blog

Application and admission

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