Project in Data Science
Syllabus, Master's level, 1DL507
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Data Science A1F, Technology A1F
- Grading system
- Fail (U), Pass (G)
- Finalised by
- The Faculty Board of Science and Technology, 3 March 2022
- Responsible department
- Department of Information Technology
150 credits including 30 credits in data science at Master's level, including Data, Ethics and Law, and including 75 credits in mathematics and computer science of which at least 15 credits in computer science. Proficiency in English equivalent to the Swedish upper secondary course English 6.
On completion of the course the student shall be able to:
- plan and carry out a project in the field of data science in a small group and within given time frames,
- analyze an open technological problem, formulate sub-problems, and find and choose solutions to these,
- independently seek, evaluate and use scientific and technical information to achieve project goals,
- demonstrate a workable solution to a complex data science task, and show that it meets a given specification,
- make assessments taking into account relevant scientific, societal and ethical aspects in the application of data science,
- in writing and orally present the assumptions, argue for the chosen solution method and present results.
Specialisation in a technological application area. Project work in groups with a focus on working on a practical data science project. The course has two possible orientations; data science students meet students from other programs who contribute with problems, subject competence and data or data sources. Alternatively, the project is carried out in a research group under the leadership of a researcher at Uppsala University. The course also teaches soft skills: ethics, group work, introduction to project and time management and presentation techniques.
Lectures, project work, supervision.
Written report (10 credits) and oral presentation (5 credits) of the project.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
This course cannot be included in the same degree as the courses Project in Data Science (1DL505) or Project in Data Science (1DL506).
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