Data Visualization New York Meetup
Data Visualization New York Meetup
Data Visualization New York is the largest offline community of data visualization professionals in the world. We gather designers, statisticians, analysts, programmers, mathematicians, data architects, start-up execs, content specialists, and all kinds of other amazing people from across the industry. We explore the full range of possibilities for how to convert raw data into visible, shareable insights.
I co-organize the Data Visualization New York Meetup with Naomi B. Robbins.
In the future, this section will be relocated to deep-linked sections of the dataviz ny website, and there won't be a need to replicate the abstracts.
December: (Amie Wilkinson): A Distance-preserving Matrix Sketch
Note: It is with deep sadness that I must announce that Lee passed away on December 10th. Instead of his presentation, we will have an informal memorial service. Anyone who wishes to share memories of Lee or describe his influence on their work is encouraged to do so. He will be sorely missed.
Dr. Amie Wilkinson presented her father's work, and Lee's collaborator Hengrui Luo joined to answer questions.
Abstract: Visualizing very large matrices involves many significant problems. Various popular solutions to these problems involve sampling, clustering, projection, or feature selection to reduce the size and complexity of the original task. An important aspect of these methods is how to preserve relative distances between points in the higher-dimensional space after reducing rows and columns to fit in a lower dimensional space. This aspect is important because conclusions based on faulty visual reasoning can be harmful. Judging dissimilar points as similar or similar points as dissimilar on the basis of a visualization can lead to false conclusions. To ameliorate this bias and to make visualizations of very large datasets feasible, we introduce a new algorithm that selects a subset of rows and columns of a rectangular matrix. This selection is designed to preserve relative distances as closely as possible. We compare our Matrix Sketch to more traditional alternatives on a variety of artificial and real datasets.
Bio: Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph.D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O.
Wilkinson is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and a Fellow of the American Association for the Advancement of Science. He has won best speaker award at the National Computer Graphics Association and the Youden prize for best expository paper in the statistics journal Technometrics. He has served on the Committee on Applied and Theoretical Statistics of the National Research Council and is a member of the Boards of the National Institute of Statistical Sciences (NISS) and the Institute for Pure and Applied Mathematics (IPAM). In addition to authoring journal articles, the original SYSTAT computer program and manuals, and patents in visualization and distributed analytic computing, Wilkinson is the author (with Grant Blank and Chris Gruber) of Desktop Data Analysis with SYSTAT. He is also the author of The Grammar of Graphics, the foundation for several commercial and open-source visualization systems (IBMRAVE, Tableau, R-ggplot2, and Python-Bokeh).
December: Laurie Frick: I Want My Data
Laurie Frick is a data artist exploring the bumpy future of data captured about us. We’ve experienced the decade when humans shifted from mysterious beings - to big data algorithms, where everything about us can be known. Rather than worry, Frick envisions a time when personal data is a peek into our unique identity and just possibly a glimpse into our future. Using her background in high-technology Frick creates large scale art works, anticipating the day when patterns of behavior become patterned artworks and the mass of data will predict our lives. Her talk and deep experience with this topic make a compelling argument for why she wants her data, will companies give it back?
Laurie Frick uses data to examine what we can know about ourselves. In her hand-built installations, drawings and small works she experiments with how we will consume the mass of data increasingly captured about us. Evidence of her engineering background and long-history in high-tech are seen in the deep data analysis and detailed explanations of how this future will unfold. Her work about the future of data were recently featured on NPR’s All Things Considered, Atlantic and Wired Magazine; she has been invited to talk at Google, SXSW, Stanford and TEDx. Recipient of numerous residencies and awards, including Samsung Research, Yaddo, Bemis and Facebook. She holds an MFA from the New York Studio School, an MBA from University of Southern California and studied at NYU’s ITP program that melded art and technology into her current data work. Recent installations include public art in downtown Austin, CapitalOne, Facebook, Texas A&M and Michigan State University. She has shown at numerous galleries in Los Angeles, in New York with inclusion in an ongoing exhibition at the Musee de Civilization in Quebec. Born in Los Angeles, she lives and works in Austin, Texas.
November: Shaaron Ainsworth: Why Should We Learn to Draw?
In recent years, there has been increasing interest in asking learners to draw visual representations for themselves. When learners pick up a pencil and paper or move a stylus on a screen they can enhance their understanding. However, to date, most studies have focussed on a narrow range of practices based upon a predominantly information-processing approach to human cognition.
In this talk, I argue that we need to develop a synthetic theoretical framework that understands learning at multiple timescales (from the millisecond to millennium) and levels (from the neuron to the society). Taking this approach leads us to recognize that drawing diagrams is not an optional "nice-to-have" but is fundamental to the way people learn. New knowledge emerges when drawing, as expressing what we currently know in external forms recruits cultural, cognitive, and sensory-motor resources that develop our own and others' understanding. Unsurprisingly, therefore we can draw to learn for many purposes: we draw to prepare, to observe, to remember, to understand and to communicate.
In this talk, I illustrate these purposes using many drawings from diverse domains, address what successful drawing looks like in each case and what support learners might need. I will also consider several open questions, such as whether everyone can draw to learn and if there are certain situations where we should avoid drawing diagrams.
Shaaron Ainsworth is a Professor of Learning Sciences at the University of Nottingham with degrees in Psychology, AI and Cognitive Science. Her research interests focus on representational learning. She is particularly interested in visual and multimodal forms of learning and learning with representational technologies such as haptics, VR, collaboration tools and Serious Games. She has published around 100 papers and book chapters on these topics, supervised 20 Ph.D. and 100 MA students and collaborated with institutions around the globe.
Ainsworth, S. E., & Scheiter, K. (2021). Learning by drawing visual representations: Potential, purposes, and practical implications. Current Directions in Psychological Science, 0(0)
Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, Kohnle, A., Ainsworth, S., & Passante, G. (2020). Sketching to support visual learning with interactive tutorials. Physical Review Physics Education Research, 16(2).
Johansson, V & Stenlund, J (20210 Making time/breaking time: critical literacy and politics of time in data visualisation. Journal of Documentation
Pande, P. Learning and expertise with scientific external representations: an embodied and extended cognition model. Phenom Cogn Sci 20, 463–482 (2021). https://doi.org/10.1007/s11097-020-09686-y
July: Jean-luc Doumont: Making sense of scales in graphs
Abstract When graphing quantitative data, most people take the scales for granted, going with what is convenient or perhaps what looks good rather than deciding on what makes sense for the data set and the message. As a result, graphs are suboptimal (when not downright misleading), obscuring relationships among data rather than revealing them.
This presentation takes a long, hard look at scales in graphs in an effort to help you (1) grasp the nature and impact of scales, (2) select the optimal scales for your graphs, and (3) display these scales in a way that helps answer the viewers' questions about the data.
Bio An engineer (Louvain) and PhD in applied physics (Stanford), Jean-luc Doumont is acclaimed worldwide for his no-nonsense approach, his highly applicable, often life-changing recommendations on a wide range of topics, and Trees, maps, and theorems, his book about “effective communication for rational minds.” For additional information, visit principiae.be .
June: Miriah Meyer: Research Through the Practice of Visualization Design
Abstract An increasingly rich body of visualization research comes from the learning that happens during the practice of visualization design.
In this talk Miriah will discuss design study—a type of inquiry grounded in visualization design practice—and the variety of research contributions that emerge from it, from new visualization techniques to novel design methods. She'll also share results from her collaborations with data analysts in a wide range of fields, as well as from our work with people analyzing their own personal data.
This is a joint event with about a dozen Data Visualization Meetup groups from around the country. Thanks to the Alan Wilson of the Utah Data Visualization Meetup for organizing this event
Bio Miriah is an associate professor in the School of Computing at the University of Utah and a faculty member in the Scientific Computing and Imaging Institute. She co-directs the Visualization Design Lab, which focuses on the design of visualization systems for helping people make sense of complex data, and on the development of methods for helping visualization designers make sense of the world. She obtained her bachelors degree in astronomy and astrophysics at Penn State University, and earned a PhD in computer science from the University of Utah. Prior to joining the faculty at Utah Miriah was a postdoctoral research fellow at Harvard University and a visiting scientist at the Broad Institute of MIT and Harvard.
Miriah is the recipient of a NSF CAREER grant, a Microsoft Research Faculty Fellowship, and a NSF/CRA Computing Innovation Fellow award. She was named a University of Utah Distinguished Alumni, both a TED Fellow and a PopTech Science Fellow, and included on MIT Technology Review's TR35 list of the top young innovators. She was also awarded an AAAS Mass Media Fellowship that landed her a stint as a science writer for the Chicago Tribune.
June: Steve Wexler: Why dashboards fail and what you can do to ensure they succeed
Why dashboards fail and what you can do to ensure they succeed And why knowing your audience is essential Abstract: You’ve invested time, money, effort and resources into creating kick-ass dashboards that will help people in your organization make better decisions, faster.
So… why are you not seeing that ROI you expected?
The biggest reason for failure is that the dashboard designers do not know what their audiences really need. In this presentation best-selling author and data visualization consultant Steve Wexler will show you how to make sure this doesn’t happen with your efforts by exploring:
Two of the greatest charts of all time and why they succeeded. A guaranteed way to engage your audience and improve performance. Two of the greatest charts of all time and why you should not make anything like them. Why your stakeholders should be your collaborators. This is a joint event with about a dozen Data Visualization Meetup groups around the US.
Bio Steve Wexler is the founder of Data Revelations, co-author of The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios, and author of the upcoming book The Big Picture: How to Use Data Visualization to Make Better Decisions—Faster.
Steve has worked with ADP, Gallup, Johnson & Johnson, Deloitte, ExxonMobil, Convergys, Consumer Reports, The Economist, SurveyMonkey, Con Edison, D&B, Marist, Cornell University, Stanford University, Tradeweb, Tiffany, McKinsey & Company, and many other organizations to help them understand and visualize their data. A winner of numerous data visualization honors and awards, Steve also serves on the advisory board to the Data Visualization Society.
His presentations and training classes combine an extraordinary level of product mastery with the real-world experience gained through developing thousands of visualizations for dozens of clients. Steve has taught thousands of people in both large and small organizations and is known for conducting his seminars with clarity, patience, and humor.
May: Annual Meet our Members Sessino (Ben, Sagar, Joyce)
Abstract: Meeting accessibility criteria alone is insufficient for presenting data in ways that connect with diverse audiences. We'll walk through a data taxonomy and best practices for information design that resonate with the ways that users process information.
Bio: Ben Elgart has 15+ years using design-thinking to solve wicked problems and create novel experiences, working as a consultant to Fortune 500 companies, pro bono to non-profits and internally at health technology start-ups. He has an MS in Human-Computer Interaction from Carnegie Mellon.
Teaching machines how to visualize data
Abstract: In this talk, we look at the different ways we can teach machines how to interpret visualizations designed for humans. Can this newly developed interpretability of visual graphics be extrapolated to let machines visualize datasets? If machines are to take over the future of designing data visualizations, what constraints and guidelines should we put in place.
Bio: Sagar Mohite is an interdisciplinary computational artist and an engineer based in New York City. His past work has been an exploration in combining principles of design and computation to generate creative outputs. He currently visualizes data at Datadog. Sagar received his Master’s Degree from New York University’s Interactive Telecommunications Program, where he focused his research on creative applications of data viz and physical computing.
Compare Distributions with Ridgeline Plots
Abstract: Ridgeline plots offer an alternative to boxplots for comparing multiple distributions. We'll discuss advantages and disadvantages of ridgeline plots vis-a-vis other means of displaying the same data, as well as variations and options for this graphical form, which has gained popularity in recent years.
Bio: Joyce Robbins, Ph.D., is Lecturer in Discipline in the Statistics Department at Columbia University, where she specializes in data visualization.
April: Jon Schwabish: Why Your Organization Needs a Data Visualization Style Guide
Every organization that produces data visualizations to communicate their research should create a data visualization style guide. A data visualization style guide does for graphs what the Chicago Manual of Style does for English grammar. It defines the components of a graph and their proper, consistent use.
Like a writing style guide, a comprehensive data visualization style guide breaks down the parts of graphs, charts, and tables to demonstrate best practices and strategies to design and style your charts. Elements like font and color, the widths of lines and style gridlines, and the use of tick marks are all choices that determine whether a graph is clear, engaging, and consistent—or whether it isn’t.
Jon presnets his case for why a data visualization style guide is useful, what a style guide should include, and steps you can follow to develop a style guide.
About the speaker Dr. Jonathan Schwabish is an economist, writer, teacher, and creator of policy-relevant data visualizations. He is considered a leading voice for clarity and accessibility in how researchers communicate their findings.
His new latest book, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks details essential strategies to create more effective data visualizations. He is on Twitter @jschwabish. (Private)
March: Richard Brath: Visualizing with Text: New ways to think about text and visualization
Some of the biggest societal challenges today are related to text: fake news, phishing emails, social media disinformation, and so on. Yet, text can sometimes be an afterthought in visualization. My new book, Visualizing with Text, (AKPeters, 2020), tackles the role of text and visualization. It starts by looking backwards and sideways to see how other domains interweave text into visualizations. Then, it assembles these as a set of textual ingredients that can be assembled into any visualization. From there, I show many different examples of new and extended visualizations. The talk will cover the history, the ingredients, and a few fun interactive examples.
Richard Brath is a long time visualization designer with Uncharted Software. At Uncharted, he has created visualizations in use by hundreds of thousands of users per day. Richard is critical of the foundational assumptions of visualization, such as the role of text or shape in visualization. 10 years ago Richard started a PhD to research some of these issues, which has culminated in the publication of the book Visualizing with Text. Richard also publishes a blog on visualization at richardbrath.wordpress.com.
February: Ben JonesAbstract: We live in a world in which many of us struggle to speak the language of data. Learn about what we need to do to enable people with relevant, foundational data skills to drive the use of data and build a data culture in our businesses and in our societies.
Bio: Ben Jones (Private) is the founder and CEO of Data Literacy, a training and education company that's on a mission to help people learn the language of data. Ben teaches data visualization at the University of Washington Foster School of Business, he's the author of Avoiding Data Pitfalls (Wiley, 2019), Data Literacy Fundamentals (DLP, 2020), and his most recent book Learning to See Data (DLP, 2020). Ben formerly led marketing for the popular Tableau Public platform at Tableau. To balance out the digital side of things, he loves hiking and backpacking on the beautiful trails of the Pacific Northwest. Ben holds a BS in Mechanical Engineering from UCLA (2000) and an MBA from California Lutheran University (2011).
January: Jessica Hullman: Visualizing Uncertainty for Better Inferences from DataAbstract: Charts, graphs, and other information visualizations amplify cognition by enabling users to visually perceive trends and differences in quantitative data. While guidelines dictate how to choose visual encodings and metaphors to support accurate perception, it is less obvious how to design visualizations that encourage rational decisions and inference. I'll motivate several challenges that must be overcome to support effective reasoning with visualizations, and describe visualizations and interactive mechanisms that can help.
Bio: Jessica Hullman (Private) is an Associate Professor of Computer Science (primary) and Journalism at Northwestern University. Her research develops new representations for data as well as new ways to interact with and evaluate visualizations for amplifying cognition and decision making. She co-directs the Midwest Uncertainty Collective, a lab devoted to better visualization techniques, tools, evaluations, and theory around how to communicate uncertainty in data, with Matt Kay. Jessica is the recipient of a Microsoft Faculty Fellowship, NSF CAREER Award, and multiple best papers at top visualization and human-computer interaction conferences, among other awards.
- 2020-12-03 : Visual Display and Analysis of Geo-referenced Data with Dr. Linda Pickle
- 2020-10-20: Data Visualization for Real-world machine learning with Julia Silge
- 2020-05-20: Changing the Way Novartis Pharmaceuticals Interacts with Data
- 2020-02-10: 4 Member Talks / abstracts and bios
- Anthony Starks: Recreating the Dubois Data Portraits
- Jonathan Martel: Stop Centering Your Data (and other rules to pass on to the masses)
- Matthias Brendler: Experience Wall
- Paul Blankley: Zenlytic: No-Code Visual Data Exploration
2019-12-09 : Randy Krum on Good Dataviz Design
- a code of ethics for infographic design
2019-11-22: Ursula Laa High Dimensional Data Visualization with tours
- The Tours technique reminds me of Kai Chang's work on animated parallel coordinates to make sense of nutrient 4D geometry from Visfest 2019. Consider adding both of these to the high dimensional data visualization post on the main Serendipidata blog
2019-07-09: Naomi Robbins : Avoiding Common Graphical Mistakes
2018-07-17: 4 Member Talks
- John Rosenfelder: Business Intelligence in Music
- Cameron Yick: "Visualizing Fast and Slow" slides / video
- (On the importance of fast feedback in our tools (building on ideas from #tools-for-thought))
- Ganes Kesari: Conversational AI: Data Storytelling for a Speech-driven World slides
- Philipp Kats: Vega: Declarative Approach to Visual Storytelilng