Remove 2001 Remove Data Warehouse Remove Metrics Remove Optimization
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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

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11 Digital Marketing “Crimes Against Humanity”

Occam's Razor

This latter category contains things that are so obviously sub-optimal that no one should be doing them any more. Sophisticated Search Engine Optimization is mandatory in our world of Bing / Yandex / Baidu / Google. Making lame metrics the measures of success: Impressions, Click-throughs, Page Views. Yet there they are.

Marketing 126
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Data Science, Past & Future

Domino Data Lab

The data governance, however, is still pretty much over on the data warehouse. Toward the end of the 2000s is when you first started getting teams and industry, as Josh Willis was showing really brilliantly last night, you first started getting some teams identified as “data science” teams. All righty.