Date: Sep 28th, 2019
Time: 1:30PM – 5:00PM EST
Location: University of North America Room 1015 12750 Fair Lakes Cir, Fairfax, VA
Presenter: Dr. David Dong, Diana Liu
We are honored to have David Dong and Diana Liu this time! Dr. Dong is a senior consultant and currently working for Freddie Mac. He will discuss the advanced Shiny app development in industry. Diana Liu is a lead data scientist from Gallup and will share her extraordinary experience in deep learning algorithms.
Dr. David Dong, Shiny App Senior Consultant working for Freddie Mac
David has been developing advanced Shiny apps for academia and enterprise users for many years. He believes Shiny can be a powerful tool to facilitate communicating and reporting across the enterprise by interactive data visualization. Combining R as backend and web tech as front end, vast potential possibilities can be achieved.
Presentation Topic:
- Introduction to Advanced Shiny App Development Using Real-World Cases
- General introduction to Shiny app, including positioning, capability and limitation
- Customizing User Interface (UI) with html, CSS and JavaScript
- Reactive Programming in Shiny
Diana Liu, Lead Data Scientist at Gallup, Inc.
Diana has been working at Gallup for over 7 years. She has extensive experience in a variety of data analytics subjects, including survey, sampling, big data and deep learning. Diana is very passionate about Recommender Systems, which are extensively used in e-Commerce, including Amazon, Netflix, and Pandora for customized products recommendation.
Presentation Topic:
- Introduction to Recommender Systems: from algorithms to applications
- Comprehensive discussion of the state-of-art recommendation algorithms and applications
- Introduction to major challenges including recommendation engines, architectures, user retention, etc.
- Evaluation of recommendation algorithms using public data
Agenda:
1:30 – 2:00 Networking
2:00 – 3:00 Shiny by Dr. David Dong
3:00 – 4:00 Recommender System by Diana Liu
4:00 – 4:30 Q & A
4:30 – 5:00 Networking
Date: Sep 28th, 2019
Time: 1:30PM – 5:00PM EST
Location: University of North America Room 1015 12750 Fair Lakes Cir, Fairfax, VA
Presenter: Dr. David Dong, Diana Liu
We are honored to have David Dong and Diana Liu this time! Dr. Dong is a senior consultant and currently working for Freddie Mac. He will discuss the advanced Shiny app development in industry. Diana Liu is a lead data scientist from Gallup and will share her extraordinary experience in deep learning algorithms.
Dr. David Dong, Shiny App Senior Consultant working for Freddie Mac
David has been developing advanced Shiny apps for academia and enterprise users for many years. He believes Shiny can be a powerful tool to facilitate communicating and reporting across the enterprise by interactive data visualization. Combining R as backend and web tech as front end, vast potential possibilities can be achieved.
Presentation Topic:
- Introduction to Advanced Shiny App Development Using Real-World Cases
- General introduction to Shiny app, including positioning, capability and limitation
- Customizing User Interface (UI) with html, CSS and JavaScript
- Reactive Programming in Shiny
Diana Liu, Lead Data Scientist at Gallup, Inc.
Diana has been working at Gallup for over 7 years. She has extensive experience in a variety of data analytics subjects, including survey, sampling, big data and deep learning. Diana is very passionate about Recommender Systems, which are extensively used in e-Commerce, including Amazon, Netflix, and Pandora for customized products recommendation.
Presentation Topic:
- Introduction to Recommender Systems: from algorithms to applications
- Comprehensive discussion of the state-of-art recommendation algorithms and applications
- Introduction to major challenges including recommendation engines, architectures, user retention, etc.
- Evaluation of recommendation algorithms using public data
Agenda:
1:30 – 2:00 Networking
2:00 – 3:00 Shiny by Dr. David Dong
3:00 – 4:00 Recommender System by Diana Liu
4:00 – 4:30 Q & A
4:30 – 5:00 Networking