After successful completion of the course, students are able to discuss legal and ethical aspects of the use of data, formulate a problem for solving it using Data Science approaches, design rigorous Data Science experiments, and interpret the results of complex data analyses.
The following topics are covered in the lectures:
- Introduction to Data Science
- Data and the data lifecycle
- Conceptual Experiment design
- Workflow paradigms
- Data management, reproducibilty and traceability
- Experiment error analysis and statistical testing
- Advanced experiment design
In addtion, two exercises will be done.
The effort breakdown is:
9 2-hour lectures: 18h
Exercise 1: 15h
Exercise 2 (incl presentation): 25h
Exam preparation: 16h
Exam: 1h
SUM: 75h
Syllabus
(all in EI8, Thu, 2-4pm c.t.)
BLOCK 1
3.10.2019 Introduction to data science - data science process -Hanbury
10.10.2019 Data and the data lifecycle, ethical and legal aspects -Hanbury
BLOCK 2
17.10.2019 Conceptual Experiment Design 1: Planning and Execution of Experiments, hypotheses, ML basics -Knees
24.10.2019 Conceptual Experiment Design 2: Planning and Execution of Experiments, hypotheses, ML basics -Knees
Exercise 1: Design an experimental workflow for a given dataset
31.10.2019 Workflow paradigms environments -Schindler, Knees
BLOCK 3
14.11.2019 Experiment Error Analysis and Statistical Testing 1 -Knees
21.11.2019 Experiment Error Analysis and Statistical Testing 2 -Knees
5.12.2019 Reproducibility and traceability 1 - Rauber
12.12.2019 Reproducibility and traceability 2 - Rauber
Exercise 2 (in groups): Reproduce experimental results from a paper
16.1.2020 Group Presentations of Exercise 2
23.1.2020 Written Exam
19.3.2020 Exam repeat
