Computational Science: Reproducible Data Science and Statistical Learning

Education facts

Code: BERN02

<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>

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.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Computational Science: Reproducible Data Science and Statistical Learning

  Lund University

  Start date: 31 August 2026

  End date: 1 November 2026

  Pace of study: 50 %