Data-driven Astronomy

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

About Course

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy. Regardless of whether you’re already a scientist, studying to become one, or just interested in how modern astronomy works ‘under the bonnet’, this course will help you explore astronomy: from planets, to pulsars to black holes. Course outline: Week 1: Thinking about data - Principles of computational thinking - Discovering pulsars in radio images Week 2: Big data makes things slow - How to work out the time complexity of algorithms - Exploring the black holes at the centres of massive galaxies Week 3: Querying data using SQL - How to use databases to analyse your data - Investigating exoplanets in other solar systems Week 4: Managing your data - How to set up databases to manage your data - Exploring the lifecycle of stars in our Galaxy Week 5: Learning from data: regression - Using machine learning tools to investigate your data - Calculating the redshifts of distant galaxies Week 6: Learning from data: classification - Using machine learning tools to classify your data - Investigating different types of galaxies Each week will also have an interview with a data-driven astronomy expert. Note that some knowledge of Python is assumed, including variables, control structures, data structures, functions, and working with files.

Course Syllabus

This module introduces the idea of computational thinking, and how big data can make simple problems quite challenging to solve. We use the example of calculating the median and mean stack of a set of radio astronomy images to illustrate some of the issues you encounter when working with large datasets.

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Course Introduction

Data-driven Astronomy In this course you will learn the basic data-driven astronomy topics: how stars and galaxies form, how our universe has evolved, and why our universe is expanding. You will also learn the basic principles of mathematics that underlie all astronomical subject matter. You will be introduced to the special concepts in astronomy, and will use these topics to build a solid foundation on which to explore the universe.Week 1 Week 2 Week 3 Week 4 Data Analysis in Python This course describes how to use Python to perform numerical analysis of astronomical data. The course assumes that you already have experience in Python programming, and that you are comfortable with basic tabular data manipulation using Python's built-in functions.Module 1 Module 2 Module 3 Module 4 Data Analysis Tools This course is designed to help you examine data analysis techniques in the Python programming language. The course focuses on the use of Python modules to access and manipulate data, as well as the use of a variety of statistical techniques to make sense of your data. You'll learn the different statistical tools that are used in the industry to make sense of your data, and how these tools are implemented in Python. You'll also learn the Pregel tool, a simple data cleaning tool, and the idea of statistical significance testing.Week 1 Week 2 Week 3 Week 4

Course Tag

Python Programming Machine Learning Applied Machine Learning SQL

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