Big data is big business
Organizations are warehouses for untapped data, and mining the right data for insights will drive your bottom line.
Big data, machine learning, and artificial intelligence 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 non-technical, highly interactive, and engaging introduction to help kick-start professionals in their understanding of these topics.
All courses include:
Who should attend
This course serves professionals at all career levels who are curious to survey the newest technological advancements across the big data, machine learning, and artificial intelligence spectrum with a practical and applied focus.
What you will learn
- Gain a firm understanding of the basics and then learn the advantages and limitations of applying machine learning and artificial intelligence 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 algorithms to disrupt their own industries.
- Become conversant on the topics of big data, machine learning, and artificial intelligence.
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.
Associate Professor, Departments of Applied Mathematics and Statistics, Whiting School of Engineering
Associate Professor, Department of Physics and Astronomy, Krieger School of Arts & Sciences
Tamas Budavari, PhD is an associate professor in Departments of Applied Mathematics and Statistics, Computer Sciences, and Physics and 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 focuses on various statistical and computational challenges in astronomy as modern detector technology is rapidly changing the way science is done.