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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Today, the most common usage of business intelligence is for the production of descriptive analytics. . Descriptive Analytics: Valuable but limited insights into historical behavior. The vast majority of financial services companies use the data within their applications for what is called “ Descriptive Analytics.”

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. One of the downsides of the role that data now plays in the modern business world is that users can be overloaded with jargon and tech-speak, which can be overwhelming.

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What is business intelligence? Transforming data into business insights

CIO Business Intelligence

BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the state of the business. Business intelligence examples Reporting is a central facet of BI and the dashboard is perhaps the archetypical BI tool.

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Understanding BI Tools in Today’s Market

Smarten

You may be interested to know that TechJury reports seven out of ten businesses rate data discovery as very important, and that the top three business intelligence trends are data visualization, data quality management and self-service business intelligence. or What is happening? And that is exactly what is happening!

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What Is Embedded Analytics?

Jet Global

We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. 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. Their dashboards were visually stunning.