SGN-54106 Computational Diagnostics, 5 cr

Implementation SGN-54106 2019-01

Description

After completing the course, the student gained a basic understanding of the definition and the meaning of computational diagnostics and its utility for data science. Case studies will be discussed illustrating the interplay between computational and statistical methods that are applied to large-scale and high-dimensional data sets from genomic and genetic experiments. Moreover, the student will learn how to practically approach such problems by using the statistical programming language R. In general, the course teaches statistical thinking in the context of data science problems, i.e., the adaptation of machine learning methods in a problem specific manner.

Lessons

Assessment scale

Numerical evaluation scale (0-5)

Requirements

To complete the course, the student is required to (all three requirements must be completed to pass the course):
a) Execute the project work (20% of the final grade)
b) Execute the weekly exercises (1 per exercises lesson, 40% of the final grade)
c) Do the final exam (40% of the final grade)

Study material