Remove 2015 Remove Data Collection Remove Data mining Remove Reporting
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MLOps and the evolution of data science

IBM Big Data Hub

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. Creating an MLOps process builds in oversight and data validation to provide good governance, accountability and accuracy of data collection.

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Mobile Marketing 2015: Rethink Customer Acquisition, Intent Targeting

Occam's Razor

Two companies, Skullcandy and TripIt, delivering on four amazing outcomes that inspire us to set the bar significantly higher for our mobile efforts in 2015 (or sooner!). Snow, skate, surf and motox reports are perfectly targeted to the potential audience's tastes. Behavior/intent targeting (this one is so cool!). Let me explain.

Marketing 144
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Magnificent Mobile Website And App Analytics: Reports, Metrics, How-to!

Occam's Razor

In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Dive into Mobile Reporting and Analysis. Dive into Mobile Reporting and Analysis. Dive into Mobile Reporting and Analysis. What do you learn from this report? What do you learn from this report?

Metrics 141
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Misleading Statistics Examples – Discover The Potential For Misuse of Statistics & Data In The Digital Age

datapine

Exclusive Bonus Content: Download Our Free Data Integrity Checklist. Get our free checklist on ensuring data collection and analysis integrity! Misleading statistics refers to the misuse of numerical data either intentionally or by error. 29, 2015, Republicans from the U.S. 3) Data fishing.