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 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>