Data science is no longer a sleepy, backroom nice-to-have. It’s an essential capability for firms that wish to find valuable business and customer insights in big data. The demand for data scientists who can analyze big data has never been greater. But, they desperately need new big data tools and technologies to become more productive, use machine learning at scale, and build models faster. The future looks bright for data science as new big data platforms, machine learning algorithms, automation, and self-service tools fill the gaps to overcome key challenges and bottlenecks.
Join guest speaker, Forrester Research analyst Mike Gualtieri and Skytree CTO and co-founder, Alexander Gray, Ph.D., to understand the key trends, challenges, and emerging solutions that portend the future of data science including:
Mike's research focuses on software technology, platforms, and practices that enable technology professionals to deliver prescient customer experiences and breakthrough operational efficiency. His key technology and platform coverage areas are big data strategy, Hadoop, advanced analytics, machine learning, data science practices, predictive apps design, and emerging technologies that make software faster and smarter. Mike has more than 25 years' experience in the industry helping firms design and develop mission-critical applications in eCommerce, insurance, banking, travel/hospitality, manufacturing, healthcare, and scientific research for organizations including NASA, eBay, Bank of America, Liberty Mutual, Nielsen, EMC, and others.
Dr. Gray is CTO at Skytree and Associate Professor in the College of Computing at Georgia Tech. His work has focused on algorithmic techniques for making machine learning tractable on massive datasets. He began working with large-scale scientific data in 1993 at NASA’s Jet Propulsion Laboratory in its Machine Learning Systems Group. He recently served on the National Academy of Sciences Committee on the Analysis of Massive Data, as a Kavli Scholar, and a Berkeley Simons Fellow, and is a frequent advisor and speaker on the topic of machine learning on big data in academia, science, and industry.