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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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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What are Citizen Analysts and How Can They Improve Your Organization?

Smarten

Self-serve BI tools provide a truly intelligent solution that suggests relationships, identifies patterns, suggests visualization techniques and formats, highlights trends and patterns, and presents predictions, so the organization can promote and encourage Citizen Analysts and advance the objectives and goals of the organization.

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Themes and Conferences per Pacoid, Episode 5

Domino Data Lab

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

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PASS Financials: past, present and the Future:

Jen Stirrup

In efficient data science, we need to take our biases off the table in order to get a true picture of the data we are looking at. Here is the Current Ratio for the past four years for PASS: Year 2019 2018 2017 2016 Quick Ratio ((Current Assets) / Current Liabilities ) 1.03 Let’s see that data for the past few years.

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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.