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A beginner's guide to data analysis in cosmology using Jupyter Notebook

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Filmed at PyData London 2017

Description Current cosmology experiments face exciting computational challenges in statistics and machine learning, with large amounts of data to process. In this beginner's session, we will use Jupyter Notebook to visualise and analyse real cosmological data, using easy to implement code examples from well known python packages such as AstroML, scipy, healpy, numpy, pymc, scikit-learn and matplotlib.

Abstract As cosmology has entered the era of precision measurement, academics face the exciting challenges of analysing large datasets, many of these common to other areas of Big Data. In recent years Python has evolved as a standard tool in astronomy and cosmology due to the availability of open source statistical analysis and machine learning packages that allow the development of robust data analysis pipelines and powerful visualisations. In this session, I’d like to demonstrate how a beginner to the field of cosmology can use open data along with the Jupyter Notebook to visualise and analyse real cosmological data, using easy to implement code examples from well known python packages such as AstroML, scipy, healpy, numpy, pymc, scikit-learn and matplotlib.

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