Experiment Design for Data Science

Submitted by webmaster on Fri, 11/22/2019 - 15:57
Course No: 
188992
Course Type: 
VU
Term: 
2019W
Weekly Hours: 
2.0
Lecturer: 
Peter Knees
Allan Hanbury
Alexander Schindler
Language: 
English
Objective: 

This course gives an introduction to data science. The emphasis is on strategies for the design of experiments, considering both workflow paradigms and aspects of reproducibility and traceability of solutions. Furthermore, knowledge about the lifecycle of data, from acquisition through processing and analysis to the long-term provision and reuse, is covered. Students are also introduced to the complex legal and ethical aspects of working with data.
 
 

Content: 

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: 18hExercise 1: 15hExercise 2 (incl presentation): 25hExam preparation: 16hExam: 1hSUM: 75h
 
 

Information: 

Syllabus
(all in EI8, Thu, 2-4pm c.t.)
BLOCK 13.10.2019 Introduction to data science - data science process -Hanbury10.10.2019 Data and the data lifecycle, ethical and legal aspects -HanburyBLOCK 217.10.2019 Conceptual Experiment Design 1: Planning and Execution of Experiments, hypotheses, ML basics  -Knees24.10.2019 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, KneesBLOCK 314.11.2019 Experiment Error Analysis and Statistical Testing 1 -Knees21.11.2019 Experiment Error Analysis and Statistical Testing 2 -Knees5.12.2019 Reproducibility and traceability 1 - Rauber12.12.2019 Reproducibility and traceability 2 - RauberExercise 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

Notes: 
Examination: 

<p>2 Exercises, Exam</p>

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