Scientific Computing for Data Analysis, 5 credits
Academic year 2022/2023
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Autumn 2022, 33%, Campus
Start date: 29 August 2022
End date: 30 October 2022
Application deadline: 19 April 2022
Application code: UU-12032 Application
Language of instruction: English
Location: Uppsala
Selection: Higher education credits in science and engineering (maximum 240 credits)
Registration: 28 July 2022 – 5 September 2022
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Spring 2023, 33%, Campus
Start date: 16 January 2023
End date: 19 March 2023
Application deadline: 17 October 2022
Application code: UU-62032 Application
Language of instruction: Swedish
Location: Uppsala
Selection: Higher education credits in science and engineering (maximum 240 credits)
Registration: 15 December 2022 – 23 January 2023
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Spring 2023, 33%, Campus
Start date: 20 March 2023
End date: 4 June 2023
Application deadline: 17 October 2022
Application code: UU-62042 Application
Language of instruction: Swedish
Location: Uppsala
Selection: Higher education credits in science and engineering (maximum 240 credits)
Registration: 16 February 2023 – 27 March 2023
Entry requirements: 60 credits including a programming course in Python (eg Computer programming I) and Algebra and Geometry/Linear Algebra I. Participation in one of the courses Introduction to Scientific Computing and Scientific Computing I. Participation in Probability and Statistics. Participation in Linear Algebra II/Linear Algebra for Data Analysis or the course can be taken in parallel.
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 or tuition fees. Formal exchange students will be exempted from tuition fees, as well as the application fee. Read more about fees.
Application fee: SEK 900
Tuition fee, first semester: SEK 10,833
Tuition fee, total: SEK 10,833
About the course
This course focuses on handling large amounts of data and is divided into three different blocks. The first block deals with stochastic simulations, the second with regression analysis and least squares methods and the third with eigenvalue problems, singular value decomposition and principal component analysis. In the field of data analysis and machine learning, many algorithms and applications are based on the methods covered in this course. We study the computational methods used when working practically with data analysis of large amounts of data.
More information
Contact
Department of Information Technology
hus 10, Lägerhyddsvägen 1
Box 337, 751 05 UPPSALA
Email: info@it.uu.se
Student counsellor
Email: studievagledare@it.uu.se