January, 2016

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Excellent Analytics Tip #27: Chase Smart Calculated Metrics!

Occam's Razor

For the last decade (#omg!), I've consistently complained about a fundamental flaw in Web Analytics tools: They incentivize one night stands , rather than engagements matching customer-intent. This leads to owners of digital experiences (insanely) expecting all visitors to their websites to convert right away – anything less than that is a failure.

Metrics 89
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Data Quality in Six Verbs

Jim Harris

Once upon a time when asked on Twitter to identify a list of critical topics for data quality practitioners, my pithy (with only 140 characters in a tweet, pithy is as good as it gets) response was, and especially since I prefer emphasizing the need to take action, to propose six critical verbs: Investigate , Communicate , Collaborate , Remediate , Inebriate , and Reiterate.

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The joys of offline data collection

Data Science and Beyond

Many modern data scientists don’t get to experience data collection in the offline world. Recently, I spent a month sailing down the northern Great Barrier Reef, collecting data for the Reef Life Survey project. In addition to being a great diving experience, the trip helped me obtain general insights on data collection and machine learning, which are shared in this article.

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24 Ultimate Data Scientists To Follow in the World Today

DataRobot Blog

Having a hero / heroine helps you navigate through the difficult times. You look up to them and then think that the problems you thought were difficult are actually trivial in nature. If people can solve and deliver at a much larger scale, you can too! Read more. The post 24 Ultimate Data Scientists To Follow in the World Today appeared first on DataRobot AI Cloud.

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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. Because individual observations have so little information, statistical significance remains important to assess.

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Deploying ML Apps using Python and Flask- Learning about Flask

MLWhiz

It has been a long time since I wrote anything on my blog. So thought about giving everyone a treat this time. Or so I think it is. Recently I was thinking about a way to deploy all these machine learning models I create in python. I searched through the web but couldn’t find anything nice and easy. Then I fell upon this book by Sebastian Rashcka and I knew that it was what I was looking for.

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Softchoice heading into 2016 leading with Emerging Partner of the Year, Nutanix

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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The End of the Beginning

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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The End of the Beginning

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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Nutanix Hyperconverged Infrastructure Enables BAE Systems to Deliver Desktops as a Service

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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8 Steps to Transformation at Speed & Scale – Your Guide to Deploying StratOps

📌Is your Data & AI transformation struggling to really impact the business? Discover the game-changing StratOps approach that: Bridges the Gap : Connect your Data & AI strategy to your operating model, to ensure alignment at every level. Prioritizes Outcomes : Focuses on concrete business outcomes from day one, rather than capabilities in isolation.