Modern astronomy builds increasingly on
large, complex data sets originating from observations and/or numerical
simulations. Analyzing and interpreting such data is of fundamental
importance in astronomy today.
This course aims
at deepening the students¿ data analysis skills through an exposure to
Data Science techniques commonly employed in astronomy. The program
includes selected topics in machine learning, exploratory data analysis
and data visualization, image analysis, statistics, and modeling. The
exact program is adapted based on the interests and needs of the
The course contains two modules.
One is a lecture module (3 cr) that consists of an introduction to a
selection of common approaches and techniques. The other is a project
module (4,5 cr) that consists of practical work in which the students
deepen and apply a set of techniques to (astronomical) data. The topic
of the project module can be chosen to complement and strengthen the
students¿ PhD programs.
After completion of the course the student should be able to:
Describe the key properties of large, multidimensional data sets using
data exploration and visualization techniques. Employ commonly-used
software to explore and visualize data.
Explain the principles and properties of specific data analysis
techniques. Discuss the properties of those techniques with respect to
each others. Demonstrate proficiency in the techniques relevant for the
chosen project work.
- Apply specific modeling
and data analysis techniques to solve problems related to large
astronomical data sets; describe and explain results of such techniques
using data visualization techniques.
- Analyze and
discuss sample size issues in designing astronomical experiments. Define
meaningful samples for experiments based on statistical arguments.
Recognize and discuss sampling biases.