IEEE Member-only icon "What is Big Data Analytics and Why Should I Care?" - Big Data Analytics Tutorial Part 1 "What is Big Data Analytics and Why Should I Care?" - Big Data Analytics Tutorial Part 1

"What is Big Data Analytics and Why Should I Care?" - Big Data Analytics Tutorial Part 1

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This is Part 2 of a 2-part tutorial from the IEEE Smart Tech Metro Area Workshop Series.

The era of big data is sometimes referred to as "the end of demographics," because everything is now being quantified and tracked across a wide variety of application domains. The essence and outcome of big data is to transform "embedded knowledge" from bytes of data into human understanding, for faster discovery, deeper insights, better decisions, and identifying the "next-best action." This tutorial examines some of the techniques from machine learning that are most helpful in extracting discoveries, insights, and better decisions through data. These techniques include unsupervised learning (e.g., clustering, principal component analysis, association mining, link analysis, regression), supervised learning (classification, decision trees, neural networks), and semi supervised learning (outlier detection, novelty and anomaly discovery). Taught by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton.

This is Part 2 of a 2-part tutorial from the IEEE Smart Tech Metro Area Workshop Series.

The era of big data is sometimes referred to as "the end of demographics," because everything is now being quantified and tracked across a wide variety of application domains. The essence and outcome of big data is to transform "embedded knowledge" from bytes of data into human understanding, for faster discovery, deeper insights, better decisions, and identifying the "next-best action." This tutorial examines some of the techniques from machine learning that are most helpful in extracting discoveries, insights, and better decisions through data. These techniques include unsupervised learning (e.g., clustering, principal component analysis, association mining, link analysis, regression), supervised learning (classification, decision trees, neural networks), and semi supervised learning (outlier detection, novelty and anomaly discovery). Taught by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton.

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