With a Master's degree in Data Science, specialising in machine learning and statistics, you will learn how to make advanced statistical analyses and use state-of-the-art machine learning methods to extract knowledge from large amounts of data. You will possess knowledge with a large area of application, from helping to solve the great societal problems of our time to working at companies of all sizes and areas to identify target groups and optimise their products and revenues.
Why this programme?
The Master's Programme in Data Science with specialisation in Machine Learning and Statistics will provide you with the knowledge, tools and skills to succeed in a wide range of different positions that involve the analysis of large amounts of data. This specialisation provides the general skills needed by a data scientist, as well as advanced skills in machine learning.
Companies such as Netflix, Amazon and Spotify collect user data to generate relevant predictions and recommendations about what their users will enjoy in the future. In order to analyse data produced by large-scale scientific experiments like the Human Genome project, astronomical charts and particle accelerators, advanced machine learning algorithms and data engineering platforms are needed. Many municipal authorities also strive to collect and integrate data from several different types of sensors (air quality, sound, water distribution, and such). In all these examples, a single profession plays a pivotal role in turning information into insight: the data scientist.
For instance, you will learn to make advanced statistical analyses and use state-of-the-art machine learning methods. In addition to learning how to extract knowledge from large amounts of information, you will also gain a robust understanding of both the mathematical foundations of data science and its computational aspects.
As part of the programme, you will also be given an opportunity to delve into the ethical and legal aspects of data science. This is important not least due to the fact that many large-scale societal issues necessitate the use of data-driven methods and AI, no matter if it is related to social welfare, climate change, healthcare or democracy.
Uppsala University also offers a rich and vital research environment in data science, with a large number of researchers from several different institutions, and the programme will prepare you for active participation in research projects, either as a PhD or in the industrial sector. In the second year of the programme, you will be given the opportunity to work alongside internationally renowned research teams within data science and with students and experts from other disciplines in a project course in applied data science.
Student profile You are someone with not only a theoretical foundation in mathematics and computer science, but also curiosity about how large and complex sets of data can be utilised to solve a variety of real-life problems.
The programme leads to a Master of Science (120 credits) with Data Science as the main field of study.
The programme consists of four main parts. At first, you and your fellow students may have different backgrounds, and therefore you can choose courses to complete your basic knowledge in computer science and mathematics. You can also choose to take core data science courses, for example on data ethics and law, statistical machine learning, the theoretical foundations of data science and data engineering.
From the end of the first year, you choose courses within the specialisation machine learning and statistics covering topics such as reinforcement learning and advanced probabilistic machine learning. The programme also includes practical activities, such as a project course done at a research lab or in collaboration with students from other programmes.
You will also conduct a Master's thesis that is required to obtain the degree and you can do it at a company or research lab.
Students are expected to participate and actively contribute to teaching sessions while also assuming responsibility for their own learning.
Instruction consists of lectures, practical assignments, seminars, and projects. A large part of the programme is spent studying on your own or in a study group outside the classroom, and as such, it is important that you take a proactive role in structuring your own studies. On a seminar, you present your ideas and discuss with your classmates regarding a course book or other study material that you are required to read before the seminar; while the teacher usually only moderates the discussion. The aim is to develop critical thinking and collaborative skills. All the students are expected to be active participants in all forms of discussions.
Courses will include exam forms such as written examinations, oral examinations, lab sessions and project assignments with group examinations, case studies and written assignments.
The programme is intimately tied to contemporary research, and the courses closely follow current developments in data science.
Data scientists can expect a strong labour market, as demand for the profession has long enjoyed strong growth. Data science is one of the most sought-after qualifications both in Sweden and internationally, and basic research in universities and academia has a large demand for data science in order to utilise the data generated by facilities such as SciLifeLab and MaxV. As such, you will be faced with a strong labour market upon completion of your degree.
In fact, several independent analysts are predicting even greater demand in the field of data science, due to the ever-increasing level of automation and digitalisation. In 2017, LinkedIn published a report on new career opportunities in the US which found that the two professions enjoying the largest increase in demand were machine learning engineer and data scientist. According to IBM, the number of data science positions will increase by 39 percent in 2020. One of the largest Swedish work union SACO has also published a prediction for the data engineering labour market in Sweden till 2023 and it shows the demand will remain high.
If you so desire, you may also choose to remain in academia and pursue PhD studies in data science. The faculty hosts several research groups, and doctoral positions are sometimes offered.
Career support During your time as a student, UU Careers offers support and guidance. You have the opportunity to take part in a variety of activities and events that will prepare you for your future career. Learn more about UU Careers.
Below you will find the details about eligibility requirements, selection criteria, and tuition fees. For information on how to apply and what documents you need to submit, check the application guide. For this programme, besides the general supporting documents, you also need to submit two programme-specific documents: 1. a CV; 2. an Application Summary Sheet.
Requirements: Academic requirements A Bachelor's degree, equivalent to a Swedish Kandidatexamen, from an internationally recognised university. Also required is:
80 credits in computer science and mathematics;
15 credits in computer science including 5 credits in introductory programming;
25 credits in mathematics including linear algebra and single variable calculus; and
5 credits in statistics and probability.
Language requirements Proficiency in English equivalent to the Swedish upper secondary course English 6. This requirement can be met either by achieving the required score on an internationally recognised test, or by previous upper secondary or university studies in some countries. Detailed instructions on how to provide evidence of your English proficiency are available at universityadmissions.se.
Students are selected based on:
an overall appraisal of previous university studies,
a curriculum vitae, and
a statement of purpose to be included in the application summary sheet.
Tuition fee-paying students and non-paying students are admitted on the same grounds but in different selection groups.
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees. Fees cover application and tuition only and do not cover accommodation, academic literature or the general cost of living. Read more about fees.