Data Science & Artificial Intelligence

     

Data Science & Artificial Intelligence Symposium

LAUNCHING A DATA DRIVEN ORGANIZATION USING ARTIFICIAL INTELLIGENCE

This event is postponed to Fall 2022 and will be delivered as an in person Symposium at Binghamton University.

DATE/TIME: Fall 2022, date and time to be decided                  

This Live Virtual Data Science and Artificial Intelligence (DS&AI) Symposium will be held in Fall 2022 as an in person event. This symposium will help you avoid the overhyped AI promises, but instead enable you to chart out a plan for your business or organization to optimize measurable milestones and outcomes. Topics such as industry 4.0 and how optimization, automation, simulation and AI converge to solve complex problems will be covered by keynote speakers and in panel sessions. You will also be able to interact with technical leaders from academia and industry and get hands-on experience in the lab sessions. Join us in this collaborative effort between Binghamton University and IBM!

• COST:
  • Standard rate: TBD
  • Binghamton University Faculty and Staff: TBD
  • Binghamton University Students: TBD
  • Certificate with 0.6 CEU's: Additional $20. Payment link to qualified participants will be sent out after the conclusion of the symposium.                  

• LOCATION: Binghamton University.
• WHO CAN ATTEND: This symposium is intended for industry professionals, academic scholars and students with interest in emerging data science and machine learning/artificial intelligence (AI).
• CREDENTIALS AND GIVEAWAYS:  
  • Participants who attend all days can receive a symposium completion certificate for 0.6  Continuing Education Units (= 6 contact hours) for the cost of $20.
  • All participants will receive free e-books, whitepapers and the IBM Cloud Services for a 90-day free trial.
  • All participants will be entered into a contest for a free online Data Science or Computer Science course of their choice delivered through Watson Continuing Professional Education.

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KEYNOTE SPEAKERS (Subject to change)

PANEL SESSIONS AND PANELISTS (Subject to change)

Panel Session 1

Optimization, Automation, Simulation, and AI converging to solve complex problems. What are the relationships and applications?

Panel Session 2:

How is Data, Analytics, Machine Learning and AI allowing us to reinvent the business models and us personally as professionals.

Panel Session 3:

Data Fabric and Governance.

Data fabric is an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. Over the last decade, developments within hybrid cloud, artificial intelligence, the internet of things (IoT), and edge computing have led to the exponential growth of big data, creating even more complexity for enterprises to manage. This has made the unification and governance of data environments an increasing priority as this growth has created significant challenges, such as data silos, security risks, and general bottlenecks to decision making. Data management teams are addressing these challenges head on with data fabric solutions. They are leveraging them to unify their disparate data systems, embed governance, strengthen security and privacy measures, and provide more data accessibility to workers, particularly their business users.

HAND-ON LABS 

Data is everywhere these days and organizations are looking for more ways to differentiate themselves through analyzing that data. Projects can quickly become complex and overmanaged as more roles are added, such as data engineer, data steward, analysts, data scientists, and administrative personnel. In this series of labs we will execute hands-on labs exercises showing new ways to both analyze data and manage analytics projects. The example use case that forms the background of the exercises is based on analyzing streaming data to gain insight on machinery performance. You will apply data governance to help analysts find and use data efficiently and consistently. You will then use that data to create machine learning predictive models that can be used in applications to help inform business decisions. Insights for how to successfully execute your own analytics projects will be shared throughout the sessions.

 

Mike McMahon: AI & Data Technical Lead, IBM

Dale Mumper (IBM):  Client Technical Specialist, Data & AI solutions architect

 

QUESTIONS