The RVA Tech Data Summit is a one-day conference dedicated to all things data engineering and data science. Richmond and the greater central Virginia area have an amazing, vibrant data science and engineering community and this conference is our chance to celebrate the practitioners in our community. This event is held at one of the city’s crown event spaces, the Virginia Museum of Science’s Dewey Gottwald Center.
2023 will be the fifth year for Data Summit. Our past versions were great events with sell-out crowds of over 400 attendees.
We are anticipating plenary keynote sessions and then multiple concurrent talks in separate tracks, at roughly 45 minutes each with ~5 planned session times throughout the day. If you would like to have Q&A time, please incorporate into the 45 minute session time allotted. We are interested in any and all topics surrounding the vast data science domain. Our audience will be about 400 people with a broad range of data science and engineering exposure ranging from students to deep practitioners to leadership.
March 30, 2023 All Day Event
Science Museum of Virginia - Dewey Gottwald Center 2301 West Leigh Street Richmond, VA 23220
ABOUT THE TEAM
The RVA Data Summit is run by a diverse team of volunteer committee members in service of the Richmond Technology Council. Volunteers and the council share a common goal of bringing the data community together.
WHO SHOULD SUBMIT?
Everyone is encouraged to submit a proposal. Our primary goal is to have excellent sessions that inform and engage our attendees. Thus, the speaker selection committee will be favoring speakers who have a record of delivering excellent talks, but we also understand that everyone has to start somewhere. If you are a first-time speaker, please make sure your proposal is compelling and communicates why you are the right person to deliver it.
We are hoping to have speaker pool that is representative of our local developer population and showcases some regional talent, but we welcome submissions from anyone anywhere. If you have an idea for a session, please submit it!
This is and has always been a practitioner focused event. We want to provide as much learning and education as possible. The focus on this is making learning accessible and helpful. This event is NOT about product showcases or hand-wavy leadership talks. Potential talks ought to be applicable to building models or managing teams of real data scientists and engineers with real world problems. All talks submitted will be viewed from that perspective.
For the 2023 RVATECH Data Summit, we are organizing our breakout tracks around multiple themes.
Machine Learning and Advanced Analytics
Description: This track will focus primarily on applied machine learning and advanced analytics applications. Topics of interest would include:
- Applied machine learning and deep learning, natural language processing, computer vision, applied predictive modeling and inferential statistics, operations optimization
- Data visualization and exploratory analysis
- Common packages and frameworks for machine learning, data science, and advanced analytics
Data Engineering, Operations, and Management
Description: This track will focus primarily on data engineering, data and data science operations, and data platforms and management. Topics of interest would include:
- Machine learning and data science operations
- Resilient pipelines for data-intensive and analytic workloads
- Data platforms to support data-intensive and analytic workloads
Data Leadership and Organization
Description: This track will focus primarily on leadership, organizational structure, team management, and workforce development for data-focused organizations. Topics of interest would include:
- Building and managing data-centric organizations
- Implementing sound data and analytics strategies at various stages of organizational maturity
- Building skills and developing an existing workforce to support modern data and analytics techniques
Description: This track will focus primarily on any topics not covered in the above tracks, including any novel/emergent focal areas for data and analytics practitioners and leaders. Topics of interest could include:
- Analytics explainability and bias
- Societal impacts of algorithmic-driven products
- Environmental impacts of machine learning research
Each of these is an important component of the data science life cycle, and includes technical and organizational challenges. All of the examples below are intended to get you started and keep the conference organized around a cohesive theme, but the sky is the limit!