Admission requirements
Knowledge of the basics of astronomy
Basic understanding of statistics
Some knowledge of Python
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 machine learning techniques including 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:
Interaction with astronomical databases using SQL and set-up and population of a basic SQL database;
Understanding basic techniques for the visualisation and analysis of large datasets, including (but not limited to) principal component analysis, kernel density estimation, classification techniques and neural networks.
Soft skills
In this course, students will be trained in the following behaviour-oriented skills:
Problem solving (recognizing and analyzing problems, solution-oriented thinking)
Analytical skills (analytical thinking, abstraction, evidence)
Structured thinking (structure, modulated thinking, computational thinking, programming)
Complex ICT-skills (data analysis, programming, simulations, complex ICT applications)
Responsibility (ownership, self-discipline, bear mistakes, accountability)
Self-regulation (independence, self-esteem, aware of own goals, motives and capacities)
Written communication (writing skills, reporting, summarizing)
Collaboration (teamwork, group support, loyalty, attendance)
Critical thinking (asking questions, check assumptions)
Creative thinking (resourcefulness, curiosity, thinking out of the box)
Integrity (honesty, moral, ethics, personal values)
Timetable
See Schedules Astronomy master 2017-2018
Mode of instruction
Lectures
Practical classes
Assessment method
Written project report
Blackboard
The course will use Blackboard but some datasets might be provided online outside of Blackboard. To have access, you need an ULCN account. More information:
Reading list
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Ivezić, Connolly, VanderPlas & Gray, ISBN: 9780691151687 (recommended)
Registration
Via uSis. More information about signing up for your classes 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: Alexandar Mechev
Remarks
None