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Defining data science in 2018

Data Science and Beyond

This article is a short summary of my understanding of the definition of data science in 2018. Even better – I still get paid for being a data scientist. But what does it mean? What do I actually do here? It’s not all about machine learning. As I was wrapping up my PhD in 2012, I started thinking about my next steps.

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Machine Learning Product Management: Lessons Learned

Domino Data Lab

I was fortunate to see an early iteration of Pete Skomoroch ’s ML product management presentation in November 2018. Pete Skomoroch, San Francisco, November 2018. Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

It is also important to have a strong test and learn culture to encourage rapid experimentation. Fractal’s 2018 Net Promoter Score is greater than 70. A properly set framework will ensure quality, timeliness, scalability, consistency, and industrialization in measuring and driving the return on investment.

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Regeneron turns to IT to accelerate drug discovery

CIO Business Intelligence

When Bob McCowan was promoted to CIO at Regeneron Pharmaceuticals in 2018, he had previously run the data center infrastructure for the $81.5 Empowering scientists through the cloud McCowan set about migrating Regeneron to Amazon Web Services in late 2018. If you are not on the cloud, you are going to be left behind.”

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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What’s driving the global common data capability at RGA

CIO Business Intelligence

If Copilot finds a misclassified 2018 payroll file on SharePoint, then it can answer questions about peoples pay. An Agile and product management mindset is also necessary to foster an experimentation approach, and to move away from the desire to control data. This can cause risk without a clear business case. Thats a critical piece.

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AI in Analytics: The NLQ Use Case

Sisense

When the app is first opened, the user may be searching for a specific song that was heard while passing by the neighborhood cafe, or the user may want to be surprised with, let’s say, a song from the new experimental album by a Yemen Reggae folk artist. when the user actually meant to compare between Q1 2018 to the whole of 2017?