Remove Data Processing Remove Metrics Remove Prescriptive Analytics Remove Visualization
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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. An e-commerce conglomeration uses predictive analytics in its recommendation engine.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

The following figure shows some of the metrics derived from the study. Strategize based on how your teams explore data, run analyses, wrangle data for downstream requirements, and visualize data at different levels. Plan on how you can enable your teams to use ML to move from descriptive to prescriptive analytics.

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Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

Her talk addressed career paths for people in data science going into specialized roles, such as data visualization engineers, algorithm engineers, and so on. Then calculate the variance divided by the mean to construct a metric for noise in decision-making. For kicks, try calculating this kind of metric within your own organization.

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The Gartner 2021 Leadership Vision for Data & Analytics Leaders Webinar Q&A

Andrew White

On January 4th I had the pleasure of hosting a webinar. It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. As such any Data and Analytics strategy needs to incorporate data sovereignty as per of its D&A governance program.

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What Is Embedded Analytics?

Jet Global

Bottom line is that analytics has migrated from a trendy feature to a got-to-have. Plus, there is an expectation that tools be visually appealing to boot. In the past, data visualizations were a powerful way to differentiate a software application. Their dashboards were visually stunning.