Statistical Programming and Open Science Methods

Time I: 
Tuesday, 09:00am

This course communicates how to develop data science applications that comply to the FAIR principles of open science. That means that they are findable, accessible, interoperable and reusable. After this course, participants should

  • be able to use common collaboration tools in software development like Git and Github,
  • understand how to use functional and object-oriented programming approaches to develop accessible code,
  • be capable to develop test routines and debug code,
  • have gained an understanding on how to profile code,
  • have developed routines for standard data analysis tasks, like data scraping, cleaning and visualization, and
  • have understood how to package statistical applications so that they are portable across platforms.

While this course is targeted at incoming doctoral researchers of the TRR 266 “Accounting for Transparency”, non TRR members at the doctoral and master level are free to attend, capacity permitting. Please apply by September 2nd by sending an email including a brief CV and your current transcript to Students will be informed about their acceptance by September 4th.

More information on the course can be found in the attached syllabus.

Fall 2019
Humboldt-Universität zu Berlin