Markup languages and reproducible programming in statistics

TESTING AND COURSE AIMS
 
1.       Developing and publishing a reproducible research compendium that contains reproducible code, data and a typeset manuscript following a markup language;
2.       Developing and publishing a reproducible, referenceable and version-controlled development repository that clearly outlines the development cycle and origin;
3.       Developing and publishing a personal repository page;


a. Students develop fundamental knowledge and understanding in the state of the art in statistical markup languages and reproducible programming and development (Knowledge and Understanding)
b. They can determine the most effective markup strategies to address a typesetting problem (Applying)
c. They can efficiently organize a reproducible programming and development process (Applying)
d. They can produce repositories up to the standards of international programming and coding conventions and initiatives (Communication)
e. They can produce publications up to academic typesetting standards, such as the typesetting standards of international peer-reviewed journals (Communication)


Assignment
Students will individually choose one statistical topic and work on a Markup manuscript about this topic. Students will need to perform calculations and program code for this script. All work for the student needs to be combined in an easy understandable and insightful data archive and materials portfolio and will need to be posted on a personal GitHub repository.
 
Details about the exact grading can be found in the course manual. 
 
To pass the course, the final grade must be 5.5 or higher, your contribution to the course should be sufficient and all assignments and practical assignments should be handed in and/or passed.

After taking this course students can understand statistical markup, statistical replication and reproducible research. Students are also able to approach challenges from different professional viewpoints. They have gained experience in marking up a professional manuscript and designing a state-of-the-art statistical archive in an open-source repository.