The course consists of two thematic blocks:

In the first block, an introduction into applications of data science in the field of sensory data collected by everyday devices like smartphones will be given. 

The following topics will be covered:  Basics of sensory data, Handling noise and missing values, feature engineering, learning based on sensory data.

In the second block, two powerful machine learning approaches will be introduced: decision trees and neural networks, as well as a number of associated topics such as generalization, model ensembles, symbolism vs connectionism, deep learning and non-linear processing. We will practice applying these methods on example data. 

All practical examples will be done in R language.

After completing this course students will have knowledge about different areas of application for data science. Students will have a basic understanding of area-specific challenges and algorithms. Besides an understanding of the problem structure, students will learn to apply mathematical and statistical tools to support decision making. Apart from that, completing this course will contribute to the students’ ability to efficiently work and communicate in a team, work on solutions for complex practical problems by using modern statistical software.

The rules on the attendance of a Continuous Assessment Course (PI) apply.

Pursuant to the general guidelines issued by the Vice-Rector for Academic Programs and Student Affairs, the attendance requirement is met if a student is present at least 80% of the time. Students who fail to meet the attendance requirement will be de-registered from the continuous assessment course with a “fail” grade.

The final grade will be computed on the basis of two assignments and a presentation.

First assignment: 40%

Second assignment: 40%

Presentation: 20%

Grades:

1: =>90%

2: =>80%

3: =>70%

4: =>60%

Successful conclusion of the course 1 of SBWL Data Science.

Please be aware that, for all courses in this SBWL, registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).

A working knowledge of the R programming language is expected.