Syllabus for Master's Programme in Data Science
Masterprogram i dataanalys
- 120 credits
- Programme code: TDA2M
- Established: 2019-10-22
- Established by: The Faculty Board of Science and Technology
- Revised: 2020-11-11
- Revised by: The Faculty Board of Science and Technology
- Reg. no: TEKNAT 2020/258
- Syllabus applies from: Autumn 2021
- Responsible faculty: Faculty of Science and Technology
- Responsible department: Department of Information Technology
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.
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
- Cambridge: CAE, CPE
Decisions and Guidelines
According to a decision taken by the Vice Chancellor 2019-06-18, Uppsala University will offer a Master's Programme in Data Science from 2020-07-01.
The programme offers a structured range of courses which lead to a Master of Science (120 credits) degree in Data Science. Students of this programme will develop a strong expertise in both the mathematical foundations of data science and its computational aspects, and will learn how to execute advanced statistical and machine learning methods on distributed and high-performance computing systems. The education also covers security as well as the ethical and legal aspects of data science. The programme has two specialisations, one in machine learning and statistics and one in data engineering, preparing the students for the two main types of data science jobs on the market.
This programme prepares students for active participation in research projects, either as graduate students or in industrial research projects, as well as for advanced professional activities in the field of data science. Students will have the possibility of collaborating with internationally-renowned research groups in data science as well as students and domain experts from other disciplines, through an applied data science project course. Uppsala University provides high-quality research and education across all the main academic disciplines, giving to the students of the programme the opportunity to explore a large number of application domains of their interest.
According to the Higher Education Act, the following applies for second-cycle studies:
Second-cycle studies shall be based fundamentally on the knowledge acquired by students during first-cycle courses and study programmes, or its equivalent.
Second-cycle studies shall involve the acquisition of specialist knowledge, aptitudes and accomplishments in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall:
- further develop the ability of students to integrate and make autonomous use of their knowledge,
- develop the students' ability to deal with complex phenomena, issues and situations, and
- develop the students' potential for professional activities that demand considerable autonomy, or for research and development work. Ordinance (2006:173).
Knowledge and understanding
For a Degree of Master (120 credits) students must
- demonstrate knowledge and understanding in their main field of study, including both broad knowledge in the field and substantially deeper knowledge of certain parts of the field, together with deeper insight into current research and development work; and
- demonstrate deeper methodological knowledge in their main field of study.
- demonstrate advanced knowledge of interdisciplinary terminology, theory, models, methods, and their limitations that are relevant in the data science area.
For a Degree of Master (120 credits) students must
- demonstrate an ability to critically and systematically integrate knowledge and to analyse, assess and deal with complex phenomena, issues and situations, even when limited information is available;
- demonstrate an ability to critically, independently and creatively identify and formulate issues and to plan and, using appropriate methods, carry out advanced tasks within specified time limits, so as to contribute to the development of knowledge and to evaluate this work;
- demonstrate an ability to clearly present and discuss their conclusions and the knowledge and arguments behind them, in dialogue with different groups, orally and in writing, in national and international contexts; and
- demonstrate the skill required to participate in research and development work or to work independently in other advanced contexts.
- define, formulate and solve data science problems independently and in groups, within a given framework;
- formulate models, problems and their solutions using machine learning and other data analysis algorithms, computers and software;
- apply mathematical theories and use, compare and evaluate different mathematical models and their applicability in areas inside different areas;
- solve an interdisciplinary problem by integrating knowledge from different disciplinary areas and collaborating with experts from other disciplines;
- use data science software; and
- present, explain and discuss various data science problems.
For a Degree of Master (120 credits) students must
- demonstrate an ability to make assessments in their main field of study, taking into account relevant scientific, social and ethical aspects, and demonstrate an awareness of ethical aspects of research and development work;
- demonstrate insight into the potential and limitations of science, its role in society and people's responsibility for how it is used; and
- demonstrate an ability to identify their need of further knowledge and to take responsibility for developing their knowledge.
- make judgments with regard to relevant scientific, social and ethical aspects of the application of data science;
- demonstrate an understanding of possibilities and limitations of data science, its role in society and people's responsibility for how it is used; and
- take initiative to broaden their field of knowledge, follow and evaluate new developments in data science and related interdisciplinary fields, including new research.
Layout of the Programme
The programme starts with courses in computer science and mathematics, to align the knowledge of students from different backgrounds, and with courses in data science, machine learning, ethics and law. From the second semester the students can also study courses from the two specialisations. The last part of the programme focuses on applying data science methods in specific domains. The programme ends with a degree project of 30 credits that can be performed within a research group at the university, at an authority or at a company.
The Master's programme in data science consists of the following specialisations:
- machine learning and statistics
- data engineering
Education in the programme builds upon the prior knowledge and experience of the students. Students are expected to participate actively in their education and take responsibility for personal learning outcomes as well as contributing to the learning of others. Academic staff in the programme have the primary responsibility for establishing foundations for active individual and collective learning. Continuos educational development builds on a respectful dialogue between students and staff, through which everyone is empowered to contribute to educational evolution and mutual learning.
In the programme's courses, a wide variety of teaching methods are used. Theoretical teaching is interspersed with practical sessions, usually computer-based, and communication training. Teaching is in close contact with current research, providing insight into scientific method and approach. Teaching and course literature is in English. The courses include formative and summative examination forms such as written exams, oral examinations, laboratory work, project work with group examination, case studies, peer reviewing, and other forms of written examination.
Upon request, the Vice Chancellor issues diplomas for the Master of Science (120 credits) with Data Science as the main field of study.
A Degree of Master is a so called general degree, which means that the student achieve the degree in its main subject according to the criteria below, regardless of the courses being part of the program or not, therefore there is a possibility also to include single subject courses in the degree.
Regulations according to Higher Education Ordinance
A Degree of Master (120 credits) is obtained after the student has completed course requirements of 120 higher education credits with a certain area of specialisation determined by each higher education institution itself, including at least 60 higher education credits with in-depth studies in the main field of study. In addition, the student must hold a Degree of Bachelor, a Degree of Bachelor of Arts in…, a professional degree worth at least 180 higher education credits or an equivalent foreign qualification.
For a Degree of Master (120 credits) students must have completed an independent project (degree project) worth at least 30 higher education credits in their main field of study, within the framework of the course requirements. The independent project may comprise less than 30 higher education credits, but not less than 15 higher education credits, if the student has already completed an independent project at the second level worth at least 15 higher education credits in their main field of study, or an equivalent project in a foreign educational programme.
A Degree of Master (60 credits) is obtained after the student has completed course requirements of 60 higher education credits with a certain area of specialisation determined by each higher education institution itself, including at least 30 higher education credits with in-depth studies in the main field of study. In addition, the student must hold a Degree of Bachelor, a Degree of Bachelor of Arts in…, a professional degree worth at least 180 higher education credits or an equivalent foreign qualification.
For a Degree of Master (60 credits) students must have completed an independent project (degree project) worth at least 15 higher education credits in their main field of study, within the framework of the course requirements.
A Degree of Master (60 credits) may, except for courses on advanced level, contain one or several courses on basic level comprising not more than 15 higher education credits. A degree of Master (120 credits) may, except for courses on advanced level, contain one or several courses on basic level comprising not more than 30 higher education credits. The course or the courses are meant to provide such additional competence as is needed for in-depth studies in the main field of study and cannot be included in the student's basic degree.
For a Degree of Master (120 credits) students must have completed an independent project (degree project) worth at least 30 higher education credits.
To be accepted to a later part of the programme the student must have gained at least 30 credits of equivalent qualifications on advanced level in addition to the degree at Bachelor's level outside the study programme. The application deadline for admission to the later part of the programme is 1 May for the autumn semester and 1 December for the spring senester.
- Latest syllabus (applies from Autumn 2021)
- Previous syllabus (applies from Autumn 2020)