Juliet Hougland
Data Vagabond
Bagged & Boosted
location_on United States
Member since 2 years
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Juliet Hougland
Specialises In
Data scientist and engineer with expertise in computational mathematics and years of hands-on machine learning and big data experience. Regular worldwide keynote and invited speaker. For many years Juliet has been a contributor to the open source community working on projects such as Apache Spark, Scalding, and Kiji.
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How to Experiment Quickly
50 Mins
Talk
Intermediate
The ‘science’ in data science refers to the underlying philosophy that you don’t know what works for your business until you make changes and rigorously measure impact. Rapid experimentation is a fundamental characteristic of high functioning data science teams. They experiment with models, business processes, user interfaces, marketing strategies, and anything else they can get their hands on. In this talk I will discuss what data platform tooling and organisational designs support rapid experimentation in data science teams.
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How to Experiment Quickly
50 Mins
Talk
Intermediate
The ‘science’ in data science refers to the underlying philosophy that you don’t know what works for your business until you make changes and rigorously measure impact. Rapid experimentation is a fundamental characteristic of high functioning data science teams. They experiment with models, business processes, user interfaces, marketing strategies, and anything else they can get their hands on. In this talk I will discuss what data platform tooling and organisational designs support rapid experimentation in data science teams.
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How to Experiment Quickly
50 Mins
Talk
Intermediate
The ‘science’ in data science refers to the underlying philosophy that you don’t know what works for your business until you make changes and rigorously measure impact. Rapid experimentation is a fundamental characteristic of high functioning data science teams. They experiment with models, business processes, user interfaces, marketing strategies, and anything else they can get their hands on. In this talk I will discuss what data platform tooling and organisational designs support rapid experimentation in data science teams.
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How to Experiment Quickly
45 Mins
Invited Talk
Intermediate
The ‘science’ in data science refers to the underlying philosophy that you don’t know what works for your business until you make changes and rigorously measure impact. Rapid experimentation is a fundamental characteristic of high functioning data science teams. They experiment with models, business processes, user interfaces, marketing strategies, and anything else they can get their hands on. In this talk I will discuss what data platform tooling and organizational designs support rapid experimentation in data science teams.
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Apache Spark for Machine Learning on Large Data Sets
45 Mins
Talk
Intermediate
Apache Spark is a general purpose distributed computing framework for distributed data processing. With MLlib, Spark's machine learning library, fitting a model to a huge data set becomes very easy. Similarly, Spark's general purpose functionality enables application of a model across a large collection of observations. We'll walk through fitting a model to a big data set using MLlib and applying a trained scikit-learn model to a large data set.
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