Remove 2016 Remove Data Science Remove Visualization
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Enterprise Data Science Workflows with AMPs and Streamlit

Cloudera

Understanding the technologies underlying these examples – both what they can do, and how they work – relied heavily on exploration and visualization. These fleshed-out web applications are representative end products of data science work. This is fortunate, because few data scientists are web developers on the side.

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The 2016 Crystal Ball – What’s Next in Data?

Alation

With the year coming to a close, many look back at the headlines that made major waves in technology and big data – from Spark to Hadoop to trends in data science – the list could go on and on. Venky Ganti, CTO & Co-Founder: Data sprawl will finally hit its threshold.

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Why the Data Journey Manifesto?

DataKitchen

We wrote the first version because, after talking with hundreds of people at the 2016 Strata Hadoop World Conference, very few easily understood what we discussed at our booth and conference session. I spent much time de-categorizing DataOps: we are not discussing ETL, Data Lake, or Data Science. Why should I care?

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DataRobot Flies Higher with Zepl Acquisition, Adding Cloud Native Notebook Solution to AI Platform

DataRobot

It 10x’s our world-class AI platform by dramatically increasing the flexibility of DataRobot for data scientists who love to code and share their expertise across teams of all skill levels. At DataRobot, we have always known that data science is a team sport. Data Exploration, Visualization, and First-Class Integration.

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Just Buying Into Modern BI and Analytics? Get Ready for Augmented Analytics, the Next Wave of Market Disruption

Rita Sallam

Machine learning automation is affecting all of enterprise software, but will completely transform how we build, analyze, and consume data and analytics. Over the past 10 years or more, visual-based data discovery tools (e.g. It will transform how users interact with data, and how they consume and act on insights.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Introduction.

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Attributing a deep network’s prediction to its input features

The Unofficial Google Data Science Blog

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. A note on visualization The most convenient way to inspect our feature importances (attributions) is to visualize them.

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