Software Engineering for the Data Scientist
I came up as a software developer and only recently have I gotten into data science. Software engineering is in my bones, but for many of my colleagues, software engineering is a bit of a mystery. That’s a problem because it affects productivity. Data scientists need software engineering skill — just not all the skills a professional software engineer needs. So what are the essential software engineering practices needed in data science?
In this #SpeedIdeation I’m calling on all software engineers, hackers, and data scientists to brainstorm on the best practices needed to write solid code in data science.
#SpeedIdeation 
The following link discusses the topic: http://www.quora.com/What-are-some-of-the-software-engineering-principles-every-data-scientist-should-know-How-do-you-learn-them
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This is a really good primer, Kyle. In the ideation session, I’d like to go a level of detail deeper and discuss recommendations on specific topics like data types, architectural patterns, debugging techniques, and code structuring.
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