Evergreen Ballroom Prefunction
Other
Evergreen A
This course will be a “hands on” introduction to sparklyr a package that facilitates a connection between R and Spark. Spark is an attractive tool for interfacing with large datasets in an interactive environment. Spark also incorporates a variety of other tools including stream processing, computing on graphs, and a distributed machine learning framework. In this course you will learn about the tools are available to R programmers via the sparklyr package.
Auditorium
Short Course (Half Day)
You’re an expert in your field. You’re passionate about the subject. So then why does it sometimes feel like such a struggle to help others really understand important concepts and results in your work? It’s not just you. Experts are now realizing that communicating statistics and data science information is surprisingly hard -- harder than most other fields, actually, and definitely not something for which most of us have received any training. Luckily, there are a few relatively straightforward strategies to master that can go a long way in making things easier. This course will be a fun and interactive look at those strategies, with opportunities to practice new skills in a safe and supportive environment. Non-native English language users and/or those who have traumatic memories of high school English class are especially welcomed.
Evergreen Ballroom Prefunction
Exhibitor
Evergreen EF
Stephanie Hicks, Johns Hopkins Bloomberg School of Public Health; Wendy Martinez, Bureau of Labor Statistics
Evergreen Ballroom Prefunction
Other
Evergreen I
Yuanshu Zou, Procter & Gamble
Tingting Zhai, University of Kentucky
Yan Xu, University of Kentucky
Kristin Lilly, Columbus State University
Monica Ahrens, The University of Iowa
Xiong Lyu, University of California, Santa Barbara
Nadeesri Wijekoon, University of Maryland Baltimore County
Jing Kersey, Georgia Southern University
Gelareh Rahimi, Carle Foundation Hospital
Xiao Yuan, Purdue University Fort Wayne
Chuyu Deng, University of Minnesota
Sofia De los Ángeles Bartels Gómez, University of Costa Rica
Marlena Bannick, University of Washington
Andrea Nicole Lane, Emory University
Kayoung Park, Old Dominion University
Rabab Elnaiem, UMBC
Gesine Alexandra Cauer, University of Washington
Amanda L Tapia, University of North Carolina
Siying Sylvia Li, IQVIA
Evergreen A
Panel Session
View Presentation Emma Benn, Icahn School of Medicine at Mount Sinai; Brittney Green, University of Cincinnati; Wendy Martinez, Bureau of Labor Statistics; Jack Miller, University of Michigan; Miles Ott, Smith College; Suzanne Thornton, Swarthmore College
Evergreen B
Barbara Nelson Stevens, CIA
Rashida Dorsey, EEOC
Evergreen G
Panel Session
Abigail Shoben, The Ohio State University
Evergreen Ballroom H
Concurrent Session
Svetlana Levitan, IBM
View Presentation Sama Winder, University of Washington
Min Shu, Stony Brook University
Evergreen EF
Christine Anderson-Cook, Los Alamos National Laboratory; Sarah Burke, The Perduco Group; Lu Lu, University of South Florida
Evergreen I
Che Smith, Advance Analytics
Judith E. Canner, California State University, Monterey Bay
Analyn Alquitran, University of Denver
View Presentation Sarah Kemp, University of Wisconsin-Madison, Applied Population Laboratory
Arielle Dror, Smith College
Marcela Alfaro Córdoba, Universidad de Costa Rica
Clara Roseberg, Smith College
Sarah Zimmermann, RTI International
Jennifer Ann Morrow, University of Tennessee
Shuxian Chen, University of Washington, The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute
Diane Rondeau, Fresenius Medical Care
Amanda Koepke, National Institute of Standards and Technology
Daphne H Liu, University of Washington
View Presentation Rechelle Jacobs, University of the Western Cape
Rashida Dorsey, EEOC
Caitlin M Cunningham, Le Moyne College
Vijayalakshmi Ramasamy, Miami University
Thembekile Shato, Saint Louis University College for Public Health and Social Justice
Evergreen Ballroom Prefunction
Yuanshu Zou, Procter & Gamble
Tingting Zhai, University of Kentucky
View Presentation Kristin Lilly, Columbus State University
Monica Ahrens, The University of Iowa
Xiong Lyu, University of California, Santa Barbara
Nadeesri Wijekoon, University of Maryland Baltimore County
Jing Kersey, Georgia Southern University
Gelareh Rahimi, Carle Foundation Hospital
Xiao Yuan, Purdue University Fort Wayne
Chuyu Deng, University of Minnesota
Sofia De los Ángeles Bartels Gómez, University of Costa Rica
Marlena Bannick, University of Washington
Andrea Nicole Lane, Emory University
Kayoung Park, Old Dominion University
Rabab Elnaiem, UMBC
Gesine Alexandra Cauer, University of Washington
View Presentation Amanda L Tapia, University of North Carolina
Siying Sylvia Li, IQVIA
Evergreen A
WSDS 2019 Data Challenge Goal To build community and challenge WSDS 2019 participants to contribute their time and talent to give back to their communities.
Focus To develop a project that contributes public awareness of how to spot disinformation.
Structure Teams will work to plan, design, and build an application or analyze complex data sets and develop informative visualizations. Teams are encouraged to be creative in how they take up this challenge.
More information and guidance will be provided during the Data Challenge Work Session Thursday, October 3. Each team will make a short presentation about their product Saturday, October 5.
Logistics Information about the data challenge will be shared via the WSDS community site.
Team size is flexible, but teams must have at least two people and no more than five people. Interorganizational teams are encouraged.
Teams must indicate their intent to participate by October 1 at 11:59 p.m. PT. All members of the team must be registered for WSDS. https://docs.google.com/forms/d/e/1FAIpQLSep4FZrKaxvw-_BV1TY_9cJsLmIsQF5HrIMh-MvIiSd2lRyxQ/viewform