As a data scientist, you will learn to extract valuable insight from one of the most important resources today - data. Use the latest machine learning methods to turn large amounts of information into big-picture knowledge.
Data science is about extracting knowledge from large amounts of data. The practical use of this skill is vast, from contributing to solving the great societal challenges of our time to helping corporations at all scales and in all fields target and optimise their product impact and revenue.
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.
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.
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, be they social welfare, climate change, healthcare or democracy, necessitate the use of data-driven methods and AI.
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.
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: a project course done at a research lab or in collaboration with students from other programmes, and a master's thesis 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.
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 science as a profession has long enjoyed strong growth, but due to the ever-increasing level of automation and digitalisation, several independent analysts are predicting even greater demand in the field. 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.
If you so desire, you may also choose to remain in academia and pursue a PhD in data science. As mentioned, the faculty hosts several research groups, and doctoral positions are sometimes offered.
Career support During your whole time as a student UU Careers offers you support and guidance. You have the opportunity to partake in a variety of career activities and events, as well as receive individual career counselling. This service is free of charge for all students at Uppsala University. Read more about UU Careers.
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 and 5 credits in introductory scientific computing;
25 credits in mathematics including linear algebra and single variable calculus; and
5 credits in statistics and probability.
Language requirements All applicants need to verify English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6"). This can be done in a number of ways, including through an internationally recognised test such as TOEFL or IELTS, or through previous upper secondary (high school) or university studies. The minimum test scores are:
IELTS: an overall mark of 6.5 and no section below 5.5
TOEFL: Paper-based: Score of 4.5 (scale 1–6) in written test and a total score of 575. Internet-based: Score of 20 (scale 0–30) in written test and a total score of 90
a total appraisal of quantity and quality of previous university studies; and
a statement of purpose (1 page).
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.
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