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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-driven-astronomy
课程评论：没有评论
课程名称：数据驱动的天文学 课程概述：科学正经历数据爆炸，而天文学在其中处于领先地位。现代望远镜每次观测产生的数据显示为太字节，并且模拟可观测宇宙所需的计算推向超级计算机的极限。为分析这些数据，科学家们需要具备计算思维，以解决问题。在本课程中，您将探讨处理大数据集的挑战，包括如何实施有效的算法、如何使用数据库管理数据，以及如何利用机器学习工具从数据中学习。课程侧重于实用技能，所有活动将在 Python 3 中完成，这是一种在天文学中广泛使用的现代编程语言。无论您是否已经是一名科学家，或正在学习成为科学家，或只是对现代天文学的运作感兴趣，本课程都将帮助您探索从行星到脉冲星再到黑洞的天文学。 课程大纲： 第一周：关于数据的思考 - 计算思维的原则 - 在射电图像中发现脉冲星 第二周：大数据使问题复杂 - 如何计算算法的时间复杂度 - 探索巨大星系中心的黑洞 第三周：使用 SQL 查询数据 - 如何使用数据库分析数据 - 调查其他太阳系中的系外行星 第四周：管理您的数据 - 如何设置数据库以管理数据 - 探索我们银河系中恒星的生命周期 第五周：从数据中学习：回归 - 使用机器学习工具调查数据 - 计算遥远星系的红移 第六周：从数据中学习：分类 - 使用机器学习工具对数据进行分类 - 研究不同类型的星系 每周还将与数据驱动天文学专家进行访谈。 注意：假设具备 Python 的一些知识，包括变量、控制结构、数据结构、函数及文件操作。
Name:Thinking about data
Description: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.
Name:Big data makes things slow
Description:In this module we explore the idea of scaling your code. Some algorithms scale well as your dataset increases, but others become impossibly slow. We look at some of the reason for this, and use the example of cross-matching astronomical catalogues to demonstrate what kind of improvements you can make.
Name:Querying your data
Description:Most large astronomy projects use databases to manage their data. In this module we introduce SQL - the language most commonly used to query databases. We use SQL to query the NASA Exoplanet database and investigate the habitability of planets in other solar systems.
Name:Managing your data
Description:This module introduces the basic principles of setting up databases. We look at how to set up new tables, and then how to combine Python and SQL to get the best out of both approaches. We use these tools to explore the life of stars in a stellar cluster.
Name:Learning from data: regression
Description:This module introduces the idea of machine learning. We look at standard methodology for running machine learning experiments, and then apply this to calculating redshifts of distant galaxies using decision trees for regression.
Name:Learning from data: classification
Description:In this final module we explore the limitations of decision tree classifiers. We then look at ensemble classifiers, using the random forest algorithm to classify images of galaxies into different types.
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.
