Knowledge and comprehension:
Be able to identify interesting Data Science opportunities, questions and data sources.
Be able to write code that extracts data from various relevant sources.
Be able to write code that manipulates and transforms data.
Be able to write code that visualize data.
Be able to write code that model relationships in data.
Be able to use code that produces insight from data, and the principles behind reproducible code/projects.
The student should be able to develop competence that adds value to data in the following five fields of Data Science:
Assessment will be based on a portfolio of obligatory assignments, and a portfolio project. The portfolio project can be submitted as a part of a group. The portfolio should showcase the ability to ask an interesting scientific or business relevant question, to gather and clean relevant data, to apply some meaningful analytical analyses, and to showcase or visualize the results in an engaging, digestible manner.
A graded scale of five marks from A to E for pass and F for fail. Only one overall grade is given for the course. There will not be a re-sit exam for this course.