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  </channel><item rdf:about="https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/">
    <title>Exploring the ChestXray14 dataset: problems – Luke Oakden-Rayner</title>
    <dc:date>2017-12-20T12:56:39+00:00</dc:date>
    <link>https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[On the problems of machine-learning and medical data.]]></description>
<dc:subject>medicine ml machinelearning data computervision</dc:subject>
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    <dc:date>2010-09-28T09:53:04+00:00</dc:date>
    <link>http://www.dataists.com/2010/09/a-taxonomy-of-data-science/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["Both within the academy and within tech startups, we’ve been hearing some similar questions lately: Where can I find a good data scientist? What do I need to learn to become a data scientist? Or more succinctly: What is data science?" Great starting point; looking forward to more from the blog.
]]></description>
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    <title>The Seven Secrets of Successful Data Scientists : Dataspora Blog</title>
    <dc:date>2010-09-03T09:18:24+00:00</dc:date>
    <link>http://dataspora.com/blog/the-seven-secrets-of-successful-data-scientists/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["...don’t confuse this kind of data exploration, where the goal is to size up the data, with building proper data plumbing, where you want robustness and maintainability. Perl and bash scripts are nice for the former, but can be a nightmare for building data pipelines." Lots of good stuff in this article; this was a highlight.
]]></description>
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