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A comprehensive guide to Feature Selection using Wrapper methods in Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In today’s era of Big data and IoT, we are easily. The post A comprehensive guide to Feature Selection using Wrapper methods in Python appeared first on Analytics Vidhya.

IoT 375
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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

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Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). NLG is a software process that transforms structured data into human-language content. Algorithm: A set of rules to follow to solve a problem or to decide on a particular action (e.g.,

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

It includes business intelligence (BI) users, canned and interactive reports, dashboards, data science workloads, Internet of Things (IoT), web apps, and third-party data consumers. Popular consumption entities in many organizations are queries, reports, and data science workloads.

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11 dark secrets of data management

CIO Business Intelligence

Philosophers and economists may argue about the quality of the metaphor, but there’s no doubt that organizing and analyzing data is a vital endeavor for any enterprise looking to deliver on the promise of data-driven decision-making. And to do so, a solid data management strategy is key.

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Delivering Low-latency Analytics Products for Business Success

Rocket-Powered Data Science

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Analytics 166
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Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.