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Organising your data

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Data-driven Astronomy

시드니 대학교

4.8(469개의 평가) | 11K명의 학생이 등록함

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.

강의 계획서 보기

배우게 될 기술

Python Programming, Machine Learning, Applied Machine Learning, SQL

검토

4.8(469개의 평가)

5 stars
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2 stars
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JM

Jul 15, 2017

One of the best courses I've done on Coursera. Just enough astronomy to understand the problems, and then go into the exercises in a step by step way, building up complexity. Couldn't stop!

AA

Jun 21, 2018

This is a well set course. I have completed one week and I loved blend of maths, astronomy and tools!Course content is not outdated, which is really important for a field like this.

수업에서

Querying your data

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.

Organising your data4:59

강사:

Tara Murphy
Associate Professor
Simon Murphy
Postdoctoral Researcher

스크립트

[MUSIC] Astronomers and other scientists often build catalogs. We might have lists of thousands to millions of objects and each object can have hundreds of properties. As datasets get large or complex, it's important to organize and access your data in an efficient way. In this module, we're going to look at one common way of organizing your data using relational databases. We'll start with a catalogue of exoplanetary systems, which are stars beyond our own solar system that are known to have planets orbiting them. By the end of the module, you'll be able to use SQL to answer fundamental scientific questions. Like how many earth sized exoplanets line the habitable zone of their host star? In other words, how many exoplanets have the potential to harbor life? The discovery of planets around stars beyond our solar system is one of the scientific breakthroughs of our lifetime. Research in this area is driven by the stream of discoveries pouring in. Many of these are being made by the Kepler space telescope using the transit method, both of which we'll hear more about in the next lecture. 2016 set a record for the biggest haul of exoplanets, when the Kepler team applied statistical validation to verify over a thousand new planets. This is only possible because they had so much exoplanetary data to work with. For each one of the thousands of exoplanets that are being discovered, there are tons of fundamental properties we want to keep track of. For example, the orbital period of the planet, what type of star it orbits, or what we think its surface temperature is. The surface temperature of a planet is important for assessing its habitability. We think of a planet as being habitable if it can possess liquid water on its surface. If a planet's extremely close to its star, then the planet's surface will be too hot and any water it has will boil or evaporate. A good example is Kepler-10b. Its orbit takes it so close to its star that its average temperature is nearly 2000 Kelvin. On the other hand, a planet orbiting very far from a star will be too cold, and will be covered in ice instead, much like the dwarf planets in our outer solar system. Each start has a so-called Goldilocks zone, where the temperature is just right for liquid water, which is more commonly called the habitable zone. One of the fundamental aspects of data driven research is being able to ask questions about our data. For example, we might want to ask how many exoplanets are smaller than Earth? Traditionally, scientists have answered these by writing short programs, maybe in Shell, Python, or the language of their choice, that read in the data from a text file and use a series of if statements to filter the data. This works when you know what questions you want to ask, and when you can write a custom script for each question. But what if we want other researchers to be able to investigate our data? We don't know in advance what calculations they might want to make, or how they might want to filter those data. We might also care about security, or data integrity, or being able to update our data regularly. The common solution to these challenges is to use a database management system, often just called a database. Databases are used nearly used anywhere for handling large datasets. In banking systems, university enrollment records or health information systems. Increasingly, databases are also being used in science. The most famous early example was the Sloan Digital Sky Survey that was pioneered by Microsoft database's guru Jim Gray. The SGSS was one of the first large projects to release all of their data online, with a full schema describing the data sets, and customizable query boxes to allow users to run their own queries on the server. It's enabled millions of users queries, and had a huge impact on the way future projects manage their data. Perhaps the most powerful feature of databases from a scientist's perspective is that you'll only need to declare your query. You don't need to worry about the implementation, the database system takes care of all of that for you. It means you can do your science without issues like scalability getting in your way. One of the key skills for working with large datasets is knowing how to choose the right solution for your particular problem. In this module we will discuss the advantages and the disadvantages of using flat files versus using databases. We'll also explain how databases organize data, and we'll show you how to query databases using SQL. You'll learn how to use SQL to explore a database of exoplanets using standard queries. This will allow you to determine how many of the exoplanets we know about so far, have the right parameters to support life. [MUSIC]

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