Computational Science: Reproducible Data Science and Statistical Learning

In this course, you will have the opportunity to work with and combine two important areas in data analysis: reproducible workflows and statistical learning. You will learn to create reports where programming code, results, and text are combined in the same document. We focus on common methods in statistical parametric modelling and machine learning.

The course is offered both as part of a programme and as a standalone course.

The teaching includes lectures, computer laboratory sessions, and project work. Participation in computer laboratory sessions and project work is mandatory. Assessment is conducted through an oral exam at the end of the course and through laboratory work and associated mandatory components.

Lectures, laboratory sessions, and project work

The course introduces you to the fundamental principles of reproducible and interoperable workflows, with a focus on practical application. You will get an overview of how to import, transform, and visualise data, where real-world data is prepared for analysis in electronic notebooks. These notebooks use tools for literate programming, analytical workflows, and version control. You will learn various methods for statistical learning. These include generalised linear regression, with maximum likelihood and Bayesian inference for parameter estimation.

You will also learn machine learning methods for regression and classification, as well as methods for dimensionality reduction and clustering. The course covers general methods for evaluating and selecting models, such as cross-validation.

Additionally, you will undertake a project where you choose appropriate methods to analyse data and conduct the analysis in a workflow that is easy to replicate and use with others. The results are summarised in a report.

Prerequisites

To be admitted to the course, students must have passed 90 credits in natural science or technical studies, including 43.5 credits in mathematics, where of 7.5 credits in statistics and 6 credits in programming, and English 6/B. or a bachelor's degree in physics and English 6/B.

Selection criteria

Seats are allocated according to: ECTS (HPAV): 100 %.

Tuition fees for non-EU/EEA citizens

Citizens of countries outside:

• The European Union (EU)
• The European Economic Area (EEA) and
• Switzerland

are required to pay tuition fees. You pay an instalment of the tuition fee in advance of each
semester.

Tuition fees, payments and exemptions

Full programme/course tuition fee: SEK 23,125
First payment: SEK 23,125

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Note that you may also need to pay an application fee, or provide proof of exemption.

Application fee

No tuition fees for citizens of the EU, EEA and Switzerland

There are no tuition fees for citizens of the European Union (EU), the European Economic Area (EEA) and Switzerland.