May 9th-11th
Data Visualization & Data Analysis Track
Explore the beauty of visualization
brought to you by the world’s most creative minds
The data visualization track at ODSC East 2023 brings together the world’s most creative minds that are changing the way we visualize, understand, and interact with data. Join a community of designers, data scientists, and developers to learn the art of storytelling, information communication, and data visualization using the latest open source tools and techniques.
We offer multiple talks, workshops, and interactive presentations to help you understand and create beautiful, insightful, and actionable data graphics and visuals.
Some of Our Past Data Visualization Speakers

Martin Frigaard
Martin is a Senior Clinical Programmer at BioMarin, where he builds dashboards and tools for making data-informed decisions. Previously, Martin built statistical tools and dashboards for the Diabetes Technology Society, a contributing author for Data Journalism in R on the Northeastern University School of Journalism blog/website, and other volunteer and non-profit organizations. He’s a data journalism instructor for California State University, Chico. Martin holds a graduate degree in Clinical Research and is passionate about data literacy and open source technologies.
Data Visualization with ggplot2(Workshop)

Stefanie Molin
Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

John Peach
A modern polymath, John holds advanced degrees in mechanical engineering, kinesiology and data science, with a focus on solving novel and ambiguous problems. As a senior applied data scientist at Amazon, John worked closely with engineering to create machine learning models to arbitrate chatbot skills, entity resolution, search, and personalization.
As a principal data scientist for Oracle Cloud Infrastructure, he is now defining tooling for data science at scale. John frequently gives talks on best practices and reproducible research. To that end, he has developed an approach to improve validation and reliability by using data unit tests and has pioneered Data Science Design Thinking. He also coordinates SoCal RUG, the largest R meetup group in Southern California.
Tired of Cleaning your Data? Have Confidence in Data with Feature Types(Workshop)

Karin Wolok
Karin is currently the leading developer community programming in the Developer Relations team at StarTree. Karin initially began her career in entertainment marketing working with the likes of names like Eminem and Live Nation. She also launched a successful professional women’s network in two major cities in the U.S., organized events for her local Data Science meetup, and helped lead a on-going hackathon to put machine learning in the hands of cancer biologists. Her journey working in data eventually let her to a position as Program Manager for Community Development for the leading graph database in the world, Neo4j. Most recently, she was brought on to StarTree to improve the adoption and success of the overall developer community.
Real-Time Analytics: Going Beyond Stream Processing with Apache Pinot(Workshop)

Max Urbany
As Max progresses through his Master’s Program, he is particularly interested in intelligent digital accessibility design, along with the ethical analysis of existing predictive models. His passion for creating quality user-centered tools drives him to understand as much as he can about end users while leveraging what data can reveal.
Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

Dan Chaney
Dan Chaney is the VP, Enterprise AI / Data Science Solutions, for Future Tech Enterprise, Inc., an award-winning global IT solutions provider. He oversees all sales, marketing, and technical activities focused on Future Tech’s comprehensive range of AI and data science workstation solutions. Prior to joining Future Tech, Dan spent 20 years at Northrop Grumman, most recently serving as the company’s Enterprise Director of IT Solution Architecture & Engineering. Dan earned his bachelor’s and master’s degrees in communication and computer science from the University of Kentucky. Dan is a Certified Information Systems Security Professional (CISSP) and adjunct instructor for the University of Louisville’s cybersecurity workforce program sponsored by the National Centers of Academic Excellence in Cybersecurity.
Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

Kristin Hempstead
Kristin has been with HP for 11 years and is currently the North America business development manager for HP’s data science and artificial intelligence solutions focusing on federal, education, and public sector customers. She has an MBA from University in South Florida with a specialization in Finance and MIS and a BS in Agriculture from the University of Georgia.
Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

Ian Johnson
Ian Johnson is a User Experience Engineer at Google. He also organizes of Bay Area d3, starts with SVG and then dives deep into d3 including DOM manipulation, categorical and quantitative scales, axis, brushes, color schemes, events and histograms. Ian likes to make sense of data by exploring it visually with D3.js!
Painting with Data: Introduction to d3.js(Half-Day Training)

Peter Spangler
Peter is a hands-on data science leader with a business focused approach to building data science solutions and telling stories with data. Experienced in translating business problems into data products using advanced statistical techniques and ML to support decision making in a variety of rapid growth environments. Scaled data science solutions for user acquisition, retention, channel optimization, revenue and fraud at Lyft, Alibaba and Citrix. Currently leading Marketing Science for Growth at Nextdoor.
Data Visualization with ggplot2(Workshop)

Hadrien Jean, PhD
Hadrien Jean is a machine learning scientist working at My Medical Assistent where he is developing deep learning models in the medical domain. He wrote the book Essential Math for Data Science (https://www.essentialmathfordatascience.com/) aimed at helping people to get the math needed in data science from a coding perspective. He previously worked at Ava on speech diarization. He also worked on a bird detection project using deep learning. He completed his Ph.D. in cognitive science at the École Normale Supérieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https://hadrienj.github.io/posts/).
Introduction to Linear Algebra for Data Science and Machine Learning With Python(Bootcamp)

Ryan Blue
Ryan is the co-creator of Apache Iceberg and spent the last decade working on big data infrastructure at Netflix, Cloudera, and now Tabular. He is an ASF member and a committer in the Apache Parquet, Avro, and Spark communities.

Fletcher Berryman
Fletcher Berryman is a lifelong geographer currently serving as a product manager for SafeGraph with a focus on international spatial data. At work and beyond, Berryman is most drawn to research questions that involve the intersection of geographers’ traditional considerations of “space and place” with modern technologies previously unavailable for use in examination, especially in developing economies. Outside of SafeGraph, Berryman is a co-chair of the world’s largest geospatial meetup (GeoNYC) and a research associate at the University of Chicago’s Center for Spatial Data Science.
Analyzing Dynamic Global Markets with Places Data(Talk)
Perform Detailed Spatial Analysis with SafeGraph and CARTO(Demo Talk)
More talks, hands-on workshop and training sessions
See all sessionsYou Will Meet
The Data Visualization track at ODSC East 2022 is where industry’s top creative minds gather to discuss and shape the most exciting trends and topics in data visualization. Whether you are a data visualization expert, or just starting your journey on better data presentation, this is the conference for you.
Thought leaders working in data science
Data visualization professionals
Graphic artists
Data scientists
UX designers
Business intelligence experts
Graphic designers
Media & publishing data storytellers
Startup founders and executives
Why Attend?
Connect with peers and top industry professionals at our many networking events to discover your next job, service, product or startup.
Immerse yourself in two days of in-depth talks and workshops on data visualisation and data science topics, tools, and languages.
With an incredible lineup, this event provides the training and insight for professionals who understand their organization’s need to deliver compelling data visualization.
Get full access to a suite of recorded presentations on-demand post conference.
Get access to the Open Data Visualization Conference plus 5 other co-located conferences including open data science, big data science, and disruptive data science.
Organizations are now understanding the need to break down big data to make it intuitive, insightful, and actionable. Be among the first to understand the power of data visualization.
Previous Sessions in Data Visualization Track
Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability
Talk: Adversarial Attacks on Deep Neural Networks
Training: Integrating Pandas with Scikit-Learn, an Exciting New Workflow
Workshop: Machine Learning for Digital Identity
Talk: Adding Context and Cognition to Modern NLP Techniques
Training: Good, Fast, Cheap: How to do Data Science with Missing Data
Workshop: Open Data Hub workshop on OpenShift
Talk: Practical AI Solutions Within Healthcare and Biotechnology
Training: Apache Spark for Fast Data Science (and Fast Python Integration!) at Scale
Workshop: Reproducible Data Science Using Orbyter
Talk: Combining Millions of Products into One Marketplace Using Computer Vision and Natural Language Processing
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