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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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11 dark secrets of data management

CIO Business Intelligence

For example, they may not be easy to apply or simple to comprehend but thanks to bench scientists and mathematicians alike, companies now have a range of logistical frameworks for analyzing data and coming to conclusions. More importantly, we also have statistical models that draw error bars that delineate the limits of our analysis.

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15 Best Data Analysis Tools You Can’t Miss in 2022

FineReport

Key features: As a professional data analysis tool, FineBI successfully meets business people’s flexible and changeable data processing requirements through self-service datasets. FineBI is supported by a high-performance Spider engine to extract, calculate and analyze a large volume of data with lightweight architecture.

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On procedural and declarative programming in MapReduce

The Unofficial Google Data Science Blog

Sawzall is a programming language developed at Google for performing aggregation over the result of complex operations on structured data. While use of Sawzall at Google is in decline today, we believe the lessons discussed here have survived the test of time and are employed by descendant systems used throughout Google.

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Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. Plus blatant overuse of intertextual parataxis.