Unleash the power of data and statistics to make the right decisions happen. We integrate statistical modeling and analysis with machine learning, data mining, and data management to give you unique skills.

The rapid development of information technologies has overwhelmed society with enormous volumes of information generated by large or complex systems from telecommunications, robotics, medicine, business, and many other fields. This master’s program meets the challenges of learning from these complex volumes by means of models and algorithms from machine learning, data mining, and other computer-intensive statistical methods. By joining us, you will increase the efficiency and productivity of the systems and make them smarter and more autonomous.

Learn to make reliable predictions

The program focuses on modern methods from machine learning and database management that use the power of statistics to build efficient models and make reliable predictions and optimal decisions. You will gain deep theoretical knowledge as well as practical experience from extensive amounts of laboratory work. If you want to complement your studies with courses at other universities, you can participate in exchange studies during the third semester.

Depending on your interests, you will work towards your thesis at a company, a governmental institution, or a research unit at LiU. There you can apply your knowledge to a real problem and meet people who use advanced data analytics in practice or you can go deeper into the research.

This program is for you if you aspire to learn how to:

• improve the ability of a mobile phone’s speech recognition software to distinguish vowels in a noisy environment
• provide early warning of a financial crisis by analyzing the frequency of crisis-related words in financial media and internet forums
• improve directed marketing by analyzing shopping patterns in supermarkets’ scanner databases
• build an effective spam filter
• estimate the effect that new traffic legislation will have on the number of deaths in road accidents
• use a complex DNA microarray dataset to learn about the risk factors of cancer
• determine the origin of an olive oil sample with the use of interactive and dynamic graphics

Syllabus and course details

The program runs over two years and encompasses 120 credits, including a thesis.

The introductory block of courses contains a course in basic statistics that is recommended for students with a background in computer science or engineering, and a course in programming that is recommended for students having a degree in statistics or mathematics. The courses Machine learning, Advanced Data Mining, Deep Learning, Big Data Analytics, Computational Statistics, and Bayesian learning constitute the core of the program.

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 program.

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.

Introduction

The rapid IT development has led to the overwhelming of society with enormous volumes of information generated by large or complex systems. Information can be stored in large databases, it can come in a streaming manner or it can be a result of the interaction between the system and the learning environment. This advanced-level program meets the challenges of learning from these complex information volumes by means of models and algorithms which enable efficient prediction, analysis, and decision making. Statistical modeling and analysis are integrated with machine learning, data mining, and data management into a solid basis for professional work with the information modeling and analysis of data in large or complex systems. The program also provides excellent qualifications for a career in research. The program leads to a master's degree in Statistics.

Aim

National Qualifications according to the Swedish Higher Education Act

Knowledge and understanding

For a Degree of Master (120 credits) the student shall

• demonstrate knowledge and understanding in Statistics, including both broad knowledge of the field and a considerable degree of specialized knowledge in certain areas of the field as well as insight into current research and development work, and
• demonstrate specialized methodological knowledge in Statistics.

Specialized knowledge in machine learning shall include modern powerful techniques for classification and regression, prediction, methods for statistical simulation and optimization, Bayesian methods, and methods for the analysis of large databases.

Competence and skills

For a Degree of Master (120 credits) the student shall

• demonstrate the ability to critically and systematically integrate knowledge and analyze, assess and deal with complex phenomena, issues and situations even with limited information
• demonstrate the ability to identify and formulate issues critically, autonomously, and creatively as well as to plan and, using appropriate methods, undertake advanced tasks within predetermined time frames and so contribute to the formation of knowledge as well as the ability to evaluate this work
• demonstrate the ability in speech and writing both nationally and internationally to report clearly and discuss his or her conclusions and the knowledge and arguments on which they are based in dialogue with different audiences, and
• demonstrate the skills required for participation in research and development work or autonomous employment in some other qualified capacity.

Judgment and approach

For a Degree of Master (120 credits) the student shall

• demonstrate the ability to make assessments in statistics informed by relevant disciplinary, social, and ethical issues and also to demonstrate awareness of ethical aspects of research and development work
• demonstrate insight into the possibilities and limitations of research, and especially research in statistics its role in society and the responsibility of the individual for how it is used, and
• demonstrate the ability to identify the personal need for further knowledge and take responsibility for his or her ongoing learning.

Local aims

Upon completing the program the students shall be able to:

• model information volumes that are generated by large or complex systems
• select a suitable model in a given context
• extract and organize large volumes of complexly structured data
• explore, summarize and present large and complex data sets by static, interactive, and dynamic graphical facilities
• use advanced software to analyze large or complex data volumes
• implement models suitable for data analysis, prediction, and decision making in some computer language
• combine data information with other sources of prior information to improve inference and prediction performance
• give examples of application areas where it is required to model information volumes that emerge from large or complex systems.
• uncover and statistically verify previously unknown patterns and trends in the data
• present a written thesis with a theoretical or an applied study of large or complex systems or data sets by means of methods from statistics and machine learning.

Research

The Division of Statistics and Machine Learning

We conduct research in the intersection of Statistics/Computer Science and host; the bachelor's program Statistik och data analyst, the international master's program Statistics and Data Mining, and the Machine Learning courses for engineers.