Big Data is Big Business.
Organizations are warehouses for untapped data—mining it for insights will drive your bottom line.
Big Data, Machine Learning, and Artificial Intelligence (AI) are at the head of the most disruptive technological revolution
affecting today's businesses. Is this just a fad, or something companies should pay close attention to? This course provides a gentle, non-technical, highly interactive and engaging introduction to help kick-start professionals in their understanding of these topics
This seminar serves professionals at all career levels who are curious to survey the newest technological advancements across the Big Data, Machine Learning and AI spectrum with a practical and applied focus.
- Gain a firm understanding of the basics and then learn the advantages and limitations of applying Machine Learning and AI in practice.
- Learn how to identify and extract undervalued Big Data opportunities within their businesses and organizations.
- Discover how to properly construct a prudent strategy to leverage Big Data by utilizing Machine Learning and AIalgorithms to disrupt their own industries.
- Become conversant on the topics of Big Data, Machine Learning and AI.
No active sessions.
Locations may vary. Please check the registration details and your email for location. Plan your visit.
JHU employees are eligible for 100% tuition remission. JHHS employees receive a 20% discount. For more information on discounts, registration, and cancellation policy, please visit our FAQ page.
Jim Kyung-Soo Liew, PhD
Assistant Professor, Carey Business School
Jim Kyung-Soo Liew, PhD, is an Assistant Professor of Finance at Johns Hopkins Carey Business School. His pioneering research focuses on the intersection of social media, big data, and financial markets. Liew serves on the Editorial Board of the Journal of Portfolio Management and as Chairman of the Johns Hopkins Innovation Factory. He received the Dean’s Award for Faculty Excellence in 2015 and 2016.
Tamas Budavari, PhD
Associate Professor, Departments of Applied Mathematics & Statistics, Computer Sciences, and Physics & Astronomy
Tamas Budavari, Ph.D. is an Associate Professor in Departments of Applied Mathematics & Statistics, Computer Sciences, and Physics & Astronomy. The core of his research is in computational statistics, Bayesian inference, low-dimensional embeddings, streaming algorithms, parallel processing on GPUs, scientific databases, and survey astronomy. He has been focusing on various statistical and computational challenges in astronomy as modern detector technology is rapidly changing the way science is done.