The virtual course "Data-Based Astronomy - Virtual Course - Coursera" is a course with different contents and offers video classes of Approx. 24 hours to complete. Explore its essential features, and click the orange button for detailed information on the Coursera e-Learning platform.
Science is experiencing a data explosion and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations needed to model our observable Universe push supercomputers to their limits. To analyze this data, scientists must be able to think computationally to solve problems. In this course, you'll investigate the challenges of working with large data sets: 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 activities will be done in Python 3, a modern programming language used in astronomy. Whether you're already a scientist, studying to become one, or just interested in how modern 'under the hood' astronomy works, this course will help you explore astronomy: from planets to pulsars to black holes. Course outline: Week 1: Thinking in data - Principles of computational thinking - Discovering pulsars in radio images Week 2: Big data slows things down - Solving the time complexity of algorithms - Exploring black holes in the centers of massive galaxies Week 3: Query data using SQL - How to use databases to analyze your data - Investigate exoplanets in other solar systems Week 4: Manage your data - How to set up databases to manage your data - Explore the cycle of lifetimes of stars in our Galaxy Week 5: Learning from the data: regression - Using machine learning tools to investigate your data - Calculating the redshifts of distant galaxies Week 6: Learning from the data: classification - Using machine learning tools to classify your data - Investigate different types of galaxies Each week you will also have an interview with an exp erto in data-driven astronomy. Note that some knowledge of Python is assumed, including variables, control structures, data structures, functions, and working with files.
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