2nd Annual BIG DATA Conference and Business Applications of Data Science Workshop
Thursday & Friday, September 28-29, 2017
CCS is pleased to announce Daniel Cohen, Senior Vice President, Emerging Payments for MasterCard Latin America and the Caribbean will offer the keynote address. Mr. Cohen is responsible for leading the development and commercialization of innovative payment solutions that can be used at the check-out counter, via the web, mobile phone, tablet, and beyond.
Big data, advanced computing, and algorithms are rapidly and profoundly changing every sphere of human activity; Airbnb, Uber, and Fitbit are just a few examples. Today, over 3.6 billion people worldwide are deeply engaged with smartphone devices, wearables, and Internet-of-things technologies while artificial intelligence also promises to create a wave of new products. Advanced computing gives us the ability to reliably and cost effectively store petabytes of data; and machine learning algorithms can crunch through massive datasets in real time to extract business intelligence and socially relevant information, giving firms new marketing tools, like mobile geo-social targeting. These tools have also empowered customers, making them more savvy in their interactions with business. The businesses, nonprofits, health care providers, government agencies, entrepreneurs, and educational institutions that harness these trends have an historic opportunity to gain an advantage over their competitors.
Day 1 – To address the challenges and opportunities these changes spawn, UM Center for Computational Science invites you to network with South Florida private- and public-sector healthcare providers, policy makers, entrepreneurs, educators and researchers, and hear panel discussions on Big Data.
Day 2 – The Big Data Conference dives deeper into the topics explored in Day 1, specifically on building data science competence at your organization. This will be an interactive workshop with examples of the possible compositions of data scientist teams, driven by the types of problems that you are looking to solve, an examination on the processes that the data need to go through from database to insight, an examination of the infrastructure needs both in terms of storage and computing power, caveats and limitations of data analyses, and more. Participants will have the opportunity to bring in their own problem as a test case, and apply the concepts discussed in the workshop by designing a pipeline for analysis to address it. Given the interactive nature of the workshop, participation is limited, so register early!
UM Newman Alumni Center, 6200 San Amaro Drive, Coral Gables, FL 33146
THURSDAY 9/28 | Free
2:00 – 5:00 PM Panel Discussions
5:30 – 7:00 PM Networking Reception
FRIDAY 9/29 | $200
9:00 AM – 12:30 PM Business Applications of Data Science Workshops
Stay tuned for more details. Registration links and program are forthcoming.
Joseph M. Johnson, PhD | Associate Professor of Marketing, UM School of Business Administration
Mitsunori Ogihara, PhD | Program Director, CCS Big Data Analytics & Data Mining Program; Professor, UM Department of Computer Science
Click here for details on the 1st Annual Big Data Conference.
Panelists Include . . .
George Bezzera, Senior Manager of Data Science | TripAdvisor
Mr. Bezzera manages a team of data scientists on applying machine learning to a wide variety of industry problems, such as recommender systems, fraud detection, revenue optimization, online advertising, and user personalization.
Linton Ward, Big Data Analytics, Power Systems | IBM
Dr. Linton Ward is an IBM Distinguished Engineer currently focusing on leadership open workloads on OpenPower systems. He has designed and optimized many hardware-software stack solutions and led hardware design teams for numerous integrated offerings, including Data Warehouse and Hadoop offerings. As a systems and solutions architect, Linton brings a unique combination of deep understanding of software needs, client experience and hardware capability. He regularly meets with clients to help them understand the solution space and their next steps.