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I've recently been trying to read more books and spend less time on the internet.
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[The following is my mini-chapter contribution to High Performance Python, 2nd Edition by Ian Ozsvald and Micha Gorelick (O'Reilly)] We had overpromised to our customers and our machine learning models were making a stream of errors in production.
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Introduction Data science continues to generate excitement and yet real-world results can often disappoint business stakeholders.
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Debate hosted at the Royal Statistical Society, 19th May 2015.
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Whitepaper about errors in A/B testing, written for Qubit.
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Talk from PyData London 2014 Most Winning A/B Test Results are Illusory Talk Summary Many people have started to suspect that their A/B testing results are not what they seem.
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Reposted from Google+ (Mar 30, 2014) So, the inevitable backlash against 'Big Data' has begun.
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It starts like a typical data science job interview - I summarise my resume and they describe their core products.