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Calendar of Events

IASC-LARS WEBINAR: Data Visualization, Theory and Applications

22 November – 1 December 2021

DATA VISUALIZATION THEORY AND APPLICATIONS

NATALIA DA SILVA, 
UNIVERSIDAD DE LA REPUBLICA, URUGUAY

INTERNATIONAL ORGANIZING COMMITTEE:

ALBA MARTÍNEZ-RUIZ, MONDAY ADENOMON, PAULO CANAS RODRIGUES, HAN-MING (HANK) WU, SARA TASKINEN, CRISTIAN GATU, LUIS FIRINGUETTI-LIMONE, DAVID MUÑOZ-NEGRON

PROGRAM

The Latin American Regional Section of the International Association for Statistical Computing (IASC-LARS), the IASC-LARS School on Computational Statistics and Data Science, the International Association for Statistical Computing (IASC), and the International Statistical Institute (ISI) are pleased to invite postgraduate and undergraduate students to attend the IASC-LARS Webinar Course “Data Visualization: Theory and Applications”. The course will be taught by Dr. Natalia da Silva from the Universidad de la
República, Uruguay, November 22-24, 2021.

The IASC-LARS Courses aim (1) to spread the knowledge base and advances in Statistical Computing in Latin American and the world, (2) to provide an overview of the state-of-the-art of the ongoing research in computational statistics, (3) to provide an overall perspective of the application of computational statistics in data science problems, (4) to present applications where computational statistics have been crucial to solve problems in real-life applications, and (5) to increase the number of researchers and practitioners in computational statistics and data science.

INSTRUCTOR:

Natalia da Silva (Instituto de Estadística, Universidad de la República).

AGENDA

(Local time: Montevideo – Uruguay, UCT -3 hours)

Monday – November 22
session 1: 09.00 – 10.30
session 2: 10.45 – 11.30

  • Fundamentals of data visualization
    • Importance of visualization.
    • Basic data visualizations: (barchart, histogram, density plot, scatterplot, boxplot).
    • Gestalt principles of visual perception: emergence, reification, multistability, invariance, laws of grouping (proximity, similarities, closure, continuity, connectedness).
    • Aesthetic mappings and visual encodings of data.
    • Use of color: how to select color based on the fundamental use cases (distinguish groups, represent data values and highlight).
    • Introduction to the grammar of graphics with ggplot2.

Tuesday – 30 November
session 1: 09.00 – 10.30
session 2: 10.45 – 11.30

  • Creating data displays in R
    • How to do basic data visualizations with ggplot2.
    • Multiple layers, facetting and tidying your data.
    • Scales and color.
    • Publication-ready plots: themes, axes and aspect ratios, annotations.
    • Additional geoms, examples to visualize uncertainty.
    • Introduction to interactive visualization with plotly.

Wednesday – 1 December
session 1: 09.00 – 10.30
session 2: 10.45 – 11.30

  • Advanced topics
    • Visualizing models: display the model in the data space, look at all members of a collection and explore the process of model fitting, not just the end result.
    • Introduction to graphical inference.
    • Visualization in high dimensions, focus on visualizing high-dimensional numerical data using linear projections.

References

  • Buja, Andreas, Dianne Cook, Heike Hofmann, Michael Lawrence, EunKyung Lee, Deborah F Swayne, and Hadley Wickham. 2009. Statistical inference for exploratory data analysis and model diagnostics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367 (1906): 4361-4383
  • Cook, Dianne, Deborah F Swayne, and Andreas Buja. 2007. Interactive and dynamic graphics for data analysis: with r and ggobi. Springer.
  • da Silva, Natalia, Dianne Cook, and Eun-Kyung Lee. 2017. Interactive graphics for visually diagnosing forest classifiers in r. arXiv preprint arXiv:1704.02502.
  • Lee, Stuart, Dianne Cook, Natalia da Silva, Ursula Laa, Earo Wang, Nick Spyrison, and H
  • Sherry Zhang. 2021. A review of the state-of-the-art on tours for dynamic visualization of high-dimensional data. arXiv preprint arXiv:2104.08016.
  • Majumder, Mahbubul, Heike Hofmann, and Dianne Cook. 2013. Validation of visual statistical inference, applied to linear models. Journal of the American Statistical Association 108 (503): 942-956.
  • Wickham, Hadley. July 2009. ggplot2: Elegant graphics for data analysis. useR. Springer.
  • Wickham, Hadley, Dianne Cook, and Heike Hofmann. 2015. Visualizing statistical models: Removing the blindfold. Statistical Analysis and Data Mining: The ASA Data Science Journal 8 (4): 203-225.
  • Wickham, Hadley, Dianne Cook, Heike Hofmann, and Andreas Buja. 2010. Graphical inference for infovis. IEEE Transactions on Visualization and Computer Graphics 16 (6): 973-979.
  • Wilke, Claus O. 2019. Fundamentals of data visualization: a primer on making informative and compelling figures. O’Reilly Media.
  • Wilkinson, Leland. 2012. The grammar of graphics. In Handbook of computational statistics, 375-414. Springer.

INSTRUCTOR
Natalia da Silva is an assistant professor at Instituto de Estadística, Universidad de la República, Montevideo, Uruguay (since 2017). She has a PhD in Statistics (2017) and a MSc in Statistics (2014) from Iowa State University. She has a BSc in Statistics (2007) and BSc in Economics (2008) from Universidad de la República. Some of her research interests are, supervised learning methods, prediction, exploratory data analysis, statistical graphics, reproducible research and meta-analysis.

Registration form

To become a IASC-LARS member, please complete the Membership Application Form.

All participants are expected to adhere to the ISI Community Principles and Conduct Policy.

More information about GoToWebinar please visit www.gotomeeting.com/webinar.
GoToWebinar application is also available for iOS, Android and Windows Phone.

Before to attend the course, please visit the GoToWebinar webpage and see the video “GoToWebinar Attendee Quick Start”. For more information, you can also visit the GoToWebinar YouTube channel.