Om kursen
Description
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 participants.
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.
Learning outcomes
After completion of the course the student should be able to:
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 participants.
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.
Learning outcomes
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.
Mer information
Jouni Kainulainen - jouni.kainulainen@chalmers.se
Kurslitteratur
Föreläsare
Jouni Kainulainen - jouni.kainulainen@chalmers.se
