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Data-driven Astronomy · LearnSpace
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Data-driven Astronomy

Курс от The University of Sydney
Средний≈ 22.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

SQLPython ProgrammingClassification AlgorithmsDatabase ManagementApplied Machine LearningComputational ThinkingBig DataDatabasesAlgorithmsMachine Learning MethodsData StructuresMachine LearningData ScienceData Processing

Программа курса

6 модулей · 67 учебных материалов

01Thinking about data15 материалов

Lesson 1: Introduction

Thinking about dataВидеоIntroduce yourself (optional)Обсуждение

Lesson 2: Course overview

Course overviewВидеоSet up your online assessmentВнешний инструмент

Учитесь у экспертов

Tara Murphy

Professor

Simon Murphy

Postdoctoral Researcher

Data-driven Astronomy
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Обучение на Coursera

≈ 22.3 ч

6 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

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Lesson 3: Pulsars

PulsarsВидеоPulsars: test your understandingЗадание

Lesson 4: Diving in - image stacking

Diving in: imaging stackingВидеоCalculating the mean stackВнешний инструмент

Lesson 5: The challenge: what went wrong?

Challenge: the median doesn't scaleВидеоHow could you improve the algorithm?Обсуждение

Lession 6: The solution: improving your method

The solution: improving your methodВидеоCalculating the median stackВнешний инструментModule summaryВидеоFurther readingЧтение

Bonus material: interview with an astronomer

Interview with Aris KarastergiouВидео
02Big data makes things slow11 материалов

Lesson 1: Introduction

Big data makes things slowВидеоWhat scaling problems have you encountered?Обсуждение

Lesson 2: Supermassive black holes

Supermassive black holesВидеоSupermassive black holes: test your understandingЗадание

Lesson 3: Diving in - crossmatching

What is cross-matching?ВидеоA naive cross-matcherВнешний инструмент

Lesson 4: Time complexity and scaling

Evaluating time complexityВидео

Lesson 5: A faster solution

A (much) faster algorithmВидеоCrossmatching with k-d treesВнешний инструментModule summaryВидео

Bonus material: interview with an astronomer

Interview with Brendon BrewerВидео
03Querying your data11 материалов

Lesson 1: Introduction

Organising your dataВидеоDo you use databases in your work?Обсуждение

Lesson 2: Exoplanets

ExoplanetsВидеоExoplanets - test your understandingЗадание

Lesson 3: Diving in - simple queries

Querying database with SQLВидеоWriting your own SQL queriesВнешний инструмент

Lesson 4: More advanced SQL queries

More advanced SQLВидео

Lesson 5: Joining tables

Joining tables in SQLВидеоJoining tables with SQLВнешний инструмент

Lesson 6: Planets in the habitable zone

Module summaryВидео

Bonus material: interview with an astronomer

Interview with Jon JenkinsВидео
04Managing your data9 материалов

Lesson 1: Introduction

Managing your big datasetsВидео

Lesson 2: The lifecycle of stars

The lifecycle of starsВидеоStars - test your understandingЗадание

Lesson 3: Diving in - databases

Setting up your own databaseВидеоSetting up your own databaseВнешний инструмент

Lesson 4: Exploring a star cluster

Exploring a star clusterВидеоCombining SQL and PythonВнешний инструментModule summaryВидео

Bonus material: interview with an astronomer

Interview with Emily PetroffВидео
05Learning from data: regression10 материалов

Lesson 1: Introduction

Learning from dataВидео

Lesson 2: The cosmological distance scale

The cosmological distance scaleВидеоCosmological distances - test your understandingЗадание

Lesson 3: Diving in - machine learning

What is machine learning?ВидеоBuilding a regression classifierВнешний инструмент

Lesson 4: Decision trees

Decision tree classifiersВидео

Lesson 5: Estimating redshifts using regression

Estimating redshifts using regressionВидеоImproving and evaluating our classifierВнешний инструментSummaryВидео

Bonus material - interview with an astronomer

Interview with Ashish MahabalВидео
06Learning from data: classification11 материалов

Lesson 1: Introduction

Classifying your dataВидео

Lesson 2: Types of galaxies

Types of galaxiesВидеоGalaxies - test your understandingЗадание

Lesson 3: Diving in - morphological classification

Morphological classification of galaxiesВидео

Lesson 4: Are decision trees good enough?

Limitations of decision tree classifiersВидеоClassify some galaxies by hand!ЧтениеReflection on galaxy classificationОбсуждение

Lesson 5: Ensemble classifiers

Improving our results with ensemble classifiersВидеоExploring machine learning classificationВнешний инструментModule summaryВидео

Bonus material: interview with an astronomer

Interview with Karen MastersВидео