Content of the course Introduction to Data Science
An increasing number of everyday devices is driven by data-driven methods. This module introduces the fundamentals of important data-driven methods. The starting point of this module will be basic definitions and algorithms. Building on this, implementations and applications of these methods are discussed. The practical exercises using real data will enable participating students to address practical questions with the methods discussed.
The course covers the following content:
The measurement problem
Representing data
Acquiring data
Cleaning data
Visualizing data
Unsupervised methods
Supervised methods
Applications
Competencies
Upon completion of this module, students will be able to:
Model measurement errors in data,
Model data appropriately for applications,
Take measurement errors into account when applying data-driven methods,
Evaluate methods,
Explain, implement, and apply data-cleaning methods,
Explain, implement, and apply simple supervised and unsupervised methods,