The following is a guest post by John Mack, Chief Marketing Officer at Zementis, a Microsoft Azure-certified provider of software solutions for predictive analytics.
Zementis has just released a series of video interviews featuring Zementis' CEO, Dr. Michael Zeller, and three prominent thought leaders in the field of data science. The videos are available for viewing on the Zementis website, which also features a downloadable transcript of each interview.
The videos cover both business and technical aspects of predictive analytics, with a focus on real-world use cases and practical information for data scientists, IT professionals, and business decision makers who work with and can benefit from predictive analytics for big data.
A brief description of the interviews is as follows:
Interview 1: Predictive Analytics for Healthcare: What's Now, What's New and What's Next
Dr. Ankur Teredesai (Professor of Computer Science & Systems, University of Washington Tacoma, Institute of Technology) describes some of the groundbreaking work that he and his team at the University of Washington are doing in predictive analytics for healthcare – not just within the data science lab, but also with patients, providers and policy makers.
Interview 2: Next-Generation Data Management Platforms for Predictive Analytics
Dr. Raghu Ramakrishnan (Technical Fellow; Head, Big Data Platforms and Cloud Information Services Lab, Microsoft) shares his perspectives on the future of machine learning and artificial intelligence, and also describes how cloud architectures can provide advantages when working with big data and predictive analytics. He also highlights some of the work that he and his team at Microsoft are doing in predictive analytics using Microsoft's Azure cloud.
Interview 3: The Art of Data Science: Crafting Success and Driving Real Business Value
Dr. Ying Li (Founder and Chief Data Scientist, EV Analysis Corporation) discusses the dynamics of the data scientist skill set and what it takes to be successful as a data scientist today. She also shares her philosophy about the "craftsmanship" of the data science profession and outlines a series of principles to guide practitioners to success, based on her extensive experience in both the lab and in business. Lastly, she opines on the future of the data science profession, and the degree to which automation may complement or supplant human activity.