The Rise of Data Science in the Age of Big Data Analytics

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The reason why Big Data is important is because we want to use it to make sense of our world. It’s tempting to think there’s some “magic bullet” for analyzing big data, but simple “data distillation” often isn’t enough, and unsupervised machine-learning systems can be dangerous (like, bringing-down-the-entire-financial-syste­m dangerous). Data Science is the key to unlocking insight from Big Data: by combining computer science skills with statistical analysis and a deep understanding of the data and problem we can not only make better predictions, but also fill in gaps in our knowledge, and even find answers to questions we hadn’t even thought of yet.

In the video presentation below, David M. Smith, Chief Community Officer at Revolution Analytics reflects on these topics and more.


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  1. I agree data distillation/refinement is not sufficient for big data analytics, but that does not imply that it is not necessary. In fact, one of the impediments to data scientist professionals actually practicing data science is unprepared and unrefined data. The process of preparing and refining data has been well established over the last two decades so data scientists should focus their time on the machine learning algorithms and use established business-grade practices and tools for data distillation/refinement.