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Clearing the Fog around Data Science and Machine Learning

The Usual Suspects in Some Unusual Places

Dr. Kirk Borne, Booz Allen Hamilton

Achieving value and insights from big data is a big problem if you start with the hypothesis that you need to achieve perfect accuracy using all of my data with the latest deep learning algorithms running in somebody’s cloud. Since the most important thing in big data is not big volume, but big value, then we should think first about our desired business outcomes and the value that these will bring to our organization: pattern discovery, pattern recognition, prediction, prescriptive insights, anomaly detection, and link (association) discovery. This talk will present some techniques for achieving fast insights and value from your data assets using some fairly typical algorithms, like clustering, graph (network) analysis, association mining, and traditional statistical modeling techniques. The twist will be that we will highlight these usual suspects in some unusual places, to illustrate that the standard use cases that you hear about for these algorithms can be extended to more exotic and atypical use cases, like yours! The hype around big data, data science, machine learning, AI, and deep learning produces a dense fog that can easily restrict the broad scope of your vision, the rising slope of your applications, and the promised hope of new opportunities. We are here to bust those myths and show you a better, simpler, and more rapid path to value and insights from your data.