The Complex World of Data Scientists and Black-Box Algorithms
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September 18, 2017
Hilary Mason is a huge name in the data science space, and she has an extensive understanding of what’s happening in this space. Today, she answers these questions for us: * What are the backgrounds of your typical data scientists? * What are key differences between software engineering and data science that most companies get wrong? * How should you measure the effectiveness of your work or your team’s work as a data scientist for the best results? * What is a good approach for creating a successful data product? * How can we peak behind the curtain of black-box deep learning algorithms? Below is a partial transcript. For the full interview, listen to the podcast episode by selecting the Play button above or by selecting this link, or you can also listen to the podcast through Apple Podcasts, Google Play, Stitcher, and Overcast. Curtis: Today we hear from one of the biggest thinkers in the data science space, someone who DJ Patil endorses on LinkedIn for data science skills. She worked at bit.ly, the url…