Scientific Computing for Data Analysis

5 credits

Course, Bachelor's level, 1TD352

Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, English

Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, English For exchange students

Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, The course will be taught in English, if needed

Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, The course will be taught in English, if needed For exchange students

Spring 2024 Spring 2024, Uppsala, 33%, On-campus, The course will be taught in English, if needed

Spring 2024 Spring 2024, Uppsala, 33%, On-campus, The course will be taught in English, if needed For exchange students

Spring 2024 Spring 2024, Uppsala, 33%, On-campus, The course will be taught in English, if needed

Spring 2024 Spring 2024, Uppsala, 33%, On-campus, The course will be taught in English, if needed For exchange students

Autumn 2024 Autumn 2024, Uppsala, 33%, On-campus, English

Autumn 2024 Autumn 2024, Uppsala, 33%, On-campus, English For exchange students

Autumn 2024 Autumn 2024, Uppsala, 33%, On-campus, English

Autumn 2024 Autumn 2024, Uppsala, 33%, On-campus, English For exchange students

Spring 2025 Spring 2025, Uppsala, 33%, On-campus, English

Spring 2025 Spring 2025, Uppsala, 33%, On-campus, English For exchange students

Spring 2025 Spring 2025, Uppsala, 33%, On-campus, English

Spring 2025 Spring 2025, Uppsala, 33%, On-campus, English For exchange students

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.

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