Recommended prerequisite for participation in the module

Participants must have a working knowledge of elementary statistical methods such as regression models and analysis of variance at the level taught e.g. in the “applied statistics” course described at http://asta.math.aau.dk/.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Basic concepts in data science and statistics such as e.g. regression models, classification models and cluster analysis
• Descriptive statistics and summaries of data including data visualization
• Best practice in data science -- in particular reproducible analyses

Skills

• Is able to apply the statistical software R for do descriptive statistics and estimate parameters in statistical models

Competences

• Is able to evaluate the applicability, including strengths and weaknesses of different data science models
• Is able to criticise a statistical analysis and the results hereof
• Is able to communicate the results of a statistical analysis to persons without prior statistical knowledge

Type of instruction

A combination of instructive videos, computer practicals and lectures

Extent and expected workload

The course gives 5 ECTS credits which means that the workload of the course is 150 hours.

The course consists of 12 half day (4 hours) sessions. These will typically be organized to run intensively in three blocks each consisting of two full days with an appropriate amount of time between blocks. Prior to the first block and between the blocks there will be tasks for participants to work on, either independently or in smaller groups. After the last block, there will be time to work on a major exercise/project to be handed in.

Exam

Exams

Name of exam	Data Science in R
Type of exam	Oral exam based on a project
ECTS	5
Permitted aids	Der henvises til den pågældende semesterbeskrivelse/modulbeskrivelse
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures