The RVA Tech Data Science Summit is a one-day conference dedicated to all things data science. Richmond and the greater central Virginia area have an amazing, vibrant data science and engineering community. It was time to have a conference to learn and celebrate our community.
2020 will be the second year for Data Science Summit. Last year’s inaugural conference was a great event with a sell-out crowd of over 400 attendees. We expect this year to be bigger and better.
We are anticipating plenary keynote sessions and then running 4 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 500 people with a broad range of data science and engineering exposure ranging from deep practitioners to folks with less exposure to implementation practices, but more managerial responsibility.
March 11, 2020 All Day Event
Science Museum of Virginia - Dewey Gottwald Center 2301 West Leigh Street Richmond, VA 23220
ABOUT THE TEAM
The RVA Data Science 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 science 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!
GUIDELINES + EXAMPLES:
For the 2020 rvatech/Summit + Data Science, we are organizing our breakout tracks around four different themes. 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!
TRACK #1: HOW TO BUILD EFFECTIVE MODELS?
This track focuses on the process of building models. Potential topics could be:
- Algorithms (whether new or proven) to build effective models.
- Statistical methods
- Tooling around building models
- Transfer Learning for NLP and Machine Vision
TRACK #2: HOW TO DO IT ETHICALLY AND WITH EXPLAINABILITY?
One of the challenges in extracting value from Machine Learning is making it understandable and actionable. Potential topics could be:
- Avoiding bias in both data and algorithms
- Advanced methods for explaining models (beyond feature importances)
- Beyond feature importances
- Data privacy (both on the user and business side)
- How to make work reproducible by others
- Understanding and preventing adversarial attacks
TRACK #3: HOW TO DO IT AT ENTERPRISE AT SCALE?
Talks on this track will provide insight into how to take machine learning models and implement them at scale. Potential topics could be:
- Managing many different models at the same time
- Building scalable data pipelines for data transformation and analytics
- Utilizing cloud frameworks for delivering data science solutions
- Mitigating Model Drift in production models
- Data Engineering as a core enabler for Data Science
TRACK #4: HOW TO MANAGE IT, LEAD IT, AND LEVERAGE IT IN THE ENTERPRISE?
Talks in this track will focus on how to effectively implement data science teams and their solutions in an enterprise. Potential topics could be:
- Building out an effective data science team for different business areas and sizes
- How to effectively set expectations, communicate, and educate about data science
- Identifying key roles outside of data science to enable teams to be more effective
- Secrets behind building models that deliver actual business value
- Becoming a data driven organization