Admission requirements
A basic understanding of statistics is expected and some knowledge of Python or R is highly desired.
Description
Astronomy is becoming ever more a data intensive science and preparing, interacting with and using databases and mining large data sets are core skills for astronomers of the future. This course will follow two strands. In one we will cover the SQL query language both for interaction with databases and for creating them with particular emphasis being placed on using the SDSS databases and their derivatives. The second strand focuses on data mining techniques among others, principal component analysis, density estimation, classification techniques and neural networks. The focus of the course is practical and will be structured around a number of practical tasks.
Course objectives
The course has two main objectives:
The students should learn how to interact with astronomical databases using SQL and set up and populate a basic SQL database themselves.
The students should acquire an understanding of basic techniques for the visualisation and analysis of large datasets. The techniques will include but not be limited to Principal Component Analysis, kernel density estimation, classification techniques and neural networks.
Timetable
See MSc schedules.
Mode of instruction
Lectures & Practical Classes.
Assessment method
Written project.
Blackboard
No, but there will be a course website available (see below).
Reading list
Suggested, not required:
- Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data by Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas & Alexander Gray. ISBN: 9780691151687.
Registration
Via uSis
More information about signing up for your classes at the Faculty of Science can be found here
Exchange and Study Abroad students, please see the Prospective students website for information on how to apply.
Contact information
Lecturer: Dr. J. (Jarle) Brinchmann
Assistant: Andrej Dvornik, MSc
Course website: Databases and Data Mining in Astronomy.
Remarks
None.