Theoretical Foundations for Data Science
Course, Master's level, 1MS047
Spring 2024 Spring 2024, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 15 January 2024–17 March 2024
- Language of instruction
- English
- Entry requirements
-
120 credits including 30 credits in mathematics och 10 credits in computer science. Participation in Introduction to Data Science. Participation in Linear Algebra for Data Science or Linear Algebra II. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
-
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.
- Application fee: SEK 900
- First tuition fee instalment: SEK 18,125
- Total tuition fee: SEK 18,125
- Application deadline
- 16 October 2023
- Application code
- UU-60509
Admitted or on the waiting list?
- Registration period
- 1 January 2024–28 January 2024
- Information on registration
Spring 2024 Spring 2024, Uppsala, 50%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 15 January 2024–17 March 2024
- Language of instruction
- English
- Entry requirements
-
120 credits including 30 credits in mathematics och 10 credits in computer science. Participation in Introduction to Data Science. Participation in Linear Algebra for Data Science or Linear Algebra II. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 1 January 2024–28 January 2024
- Information on registration
About the course
This course anchors decision procedures from data science in a mathematical-statistical framework.
Contact
- Study counselling
- studievagledare@math.uu.se
- +46 18 471 32 03, +46 18 471 32 00