Juliet Hougland
Data VagabondBagged & Boosted
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.
Talks I've Given
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How to Experiment Quickly
Featuring Juliet Hougland
big-dataThe ‘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,...
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How to Experiment Quickly
Featuring Juliet Hougland
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,...
big-data -
How to Experiment Quickly
Featuring Juliet Hougland
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,...
big-data -
How to Experiment Quickly
Featuring Juliet Hougland
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...
practice -
Apache Spark for Machine Learning on Large Data Sets
Featuring Juliet Hougland
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...
data