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Breaking Data Science Open by Christine Doig, Michele Chambers, Ian Stokes-Rees

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Chapter 3. Data Science for All

Thanks to the promise of new insights for innovation and competitiveness from Big Data, data science has gone mainstream. Executives are spending billions of dollars collecting and storing data, and they are demanding return on their investment. Simply getting the data faster is of limited value, so they are seeking to use the data to enrich their day-to-day operations and get better visibility into the future.

Data science is the path to monetizing the mounds of data now available. But old-school tools are laden with technical hurdles and huge costs that don’t align well with the the needs of Big Data analysis and aren’t agile enough to keep up with the almost continuously evolving demands driven by changes in the Big Data stack and in the marketplace.

Enter Open Data Science. Open Data Science is a big tent that welcomes and connects many data science tools together into a coherent foundation that enables the modern data science team to solve today’s most challenging problems. Open Data Science makes it easy for modern data science teams to use all data—big, small, or anything in between. Open Data Science also maximizes the plethora of computing technologies available, including multicore CPUs, GPUs, in-memory architectures, and clusters. Open Data Science takes advantage of a vast array of robust and reliable algorithms, plus the latest and most innovative algorithms available. This is why Open Data Science is being used to propel science, business, ...

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