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Lund University
Computational Science: Reproducible Data Science and Statistical Learning
<p>Interested in basic principles of reproducible and interoperable work flows with a clear focus on application? Learn how to import, transform and visualize date using electronic "notebooks".</p><p>BERN02. The overall learning outcome for the course is to let the students work with and<br /> combine two fields f…
- Higher education
- Information unavailable
- 31 August 2026
- Information unavailable
- Information unavailable
- 50 %
Overview
<p>Interested in basic principles of reproducible and interoperable work flows with a clear focus on application? Learn how to import, transform and visualize date using electronic "notebooks".</p><p>BERN02. The overall learning outcome for the course is to let the students work with and<br /> combine two fields for data analysis in computational science: reproducible work<br /> flows and statistical learning. This includes to be able to create reports where<br /> programming code, results and text are combined in the same document, applied on<br /> a selection of common methods in statistical parametric modelling and machine<br /> learning. The course introduces basic principles of reproducible and interoperable work flows<br /> with a clear focus on application. The students will obtain an overview in import,<br /> transformation and visualisation of data, where realistic data are prepared for analysis<br /> in electronic "notebooks". These electronic "notebooks" use tools for "literate<br /> programming", analytical work flows and version management.</p>
Admission scores
Entry requirements
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.
The text is reproduced from the Susa source. Antagningsdata does not map GY11 and GY25 or assess personal eligibility.
Source, measure and data quality
- Source
- Skolverket Susa-navet
- Period
- 2026-08-31
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.lu.bern02.12750.20262
- Last checked
- 2026-09-23T10:38:26.989764+00:00
- Limitation
- Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.
Programme content
Study structure
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About the provider
Sources and data quality
Education facts for the selected offering come from Skolverket Susa-navet.
Retrieved . Published . Times are shown in Swedish local time.
Source identity and publication version
- Publication version
- 8e217193-f5fa-4778-b085-a4521fd03e8d
- Education identity in the source
- i.uoh.lu.bern02.12750.20262
- Offering identity in the source
- e.uoh.lu.bern02.12750.20262
- Education-form source code
- HS
- Education code in the source
- BERN02
- Change time according to the source
- 2026-02-03T11:29:39
The provider, education and education offering are separate identities. Application information should be checked on the official website. Supplementary statistics have not been obtained from this source.