R User Day at Data Day Texas

Advance discount tickets are still available. Don't wait and pay more.

For several years, we've received a multitude of requests from the data community to increase the Data Day coverage of the R language and environment (wikipedia). This year, we decided to do something about it.

Imagine if Austin had a world class R User Conference.

Early this spring, our friend Daniel Woodie, took over as organizer of the Austin R User Group. Seeing the great job he is doing to revive the group and bring in good content, we asked him if he would help us curate an R User track for Data Day Texas. After reviewing our lists of potential speakers, we decided not to limit R to an individual track, but to go all the way -- and create a mini R conference within Data Day Texas. We will set aside an entire portion of the conference facility aside for the the R community. You will not need to buy a separate ticket for R User Day. Your Data Day Texas ticket gets you into all the content for R User Day as well.

Sessions, Workshops, Proposals and Sponsorship

We'll begin to announce the specific sessions and workshops over the next few weeks. We are still accepting proposals. If you would like to present at R User Day, visit our proposals page for details.
http://datadaytexas.com/2018/proposals

We have special sponsorship categories for companies supporting the R community. For details on sponsorship, visit our sponsors page for details:
http://datadaytexas.com/2018/sponsors

Confirmed Speakers for R User Day

We are just beginning to announce speakers. Expect to see many more listed over the next few weeks.

Chester Ismay (Portland) @old_man_chester

Chester Ismay (LinkedIn / GitHub) is an Adjunct Assistant Professor at Pacific University, and an Instructional Technologist and Consultant for Data Science, Statistics, and R at Reed College.

Jared Lander (NYC) @jaredlander

Jared Lander (LinkedIn) is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts.
Jared specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Hilary Parker (San Francisco) @hspter

Hilary Parker (LinkedIn / GitHub) is a Data Scientist at Stitch Fix and co-host of the Not So Standard Deviations podcast. She is an R and statistics enthusiast determined to bring rigor to analysis wherever she goes. At Stitch Fix she works on teasing apart correlation from causation, with a strong dose of reproducibility. Formerly a Senior Data Analyst at Etsy, she received a PhD in Biostatistics from the Johns Hopkins Bloomberg School of Public Health.

Gabriela de Queiroz (San Francisco) @gdequeiroz

Gabriela de Queiroz (LinkedIn / GitHub) is the Lead Data Scientist at SelfScore. Formerly Gabriela was data scientist at Sharethrough, where she developed statistical models from concept creation to production, designed, ran, and analyzed experiments, and employed a variety of techniques to derive insights and drive data-centric decisions. Gabriela is the founder of R-Ladies, an organization created to promote diversity in the R community, which now has over 25 chapters worldwide. Currently, she is developing an online course on machine learning in partnership with DataCamp.

David Robinson (NYC) @drob

David Robinson (LinkedIn / GitHub) is a data scientist at Stack Overflow with a PhD in Quantitative and Computational Biology from Princeton University. He enjoys developing open source R packages, including broom, gganimate, fuzzyjoin and widyr, as well as blogging about statistics, R, and text mining on his blog, Variance Explained.

Julia Silge (Salt Lake City) @juliasilge

Julia Silge (LinkedIn / GitHub) is a data scientist at Stack Overflow. She enjoys making beautiful charts, the statistical programming language R, black coffee, red wine, and the mountains of her adopted home here in Utah. She has a PhD in astrophysics and an abiding love for Jane Austen. Her work involves analyzing and modeling complex data sets while communicating about technical topics with diverse audiences.

David Smith (Chicago) @revodavid

David Smith is the R Community Lead at Microsoft. With a background in data science, he writes daily about applications of predictive analytics at the Revolutions blog (blog.revolutionanalytics.com), and is a co-author of Introduction to R.

Daniel Woodie (Austin) @DanielWoodie5

Daniel Woodie is founder and lead scientist of Bamboo Analytics, a data science services firm. He's trained originally as a statistician and has worked on applications ranging from systems neuroscience to global supply chains. With Bamboo Analytics he offers analytical consulting and training to early stage startups and Fortune 500 companies, alike.
Daniel will be emcee for R User Day at Data Day Texas.

Author Signings at R User Day

Text Mining with R (O'Reilly Media)

Text Mining with R, by Julia Silge and David Robinson, shows you how to manipulate, summarize, and visualize the characteristics of text, sentiment analysis, tf-idf, and topic modeling. Along with tidy data methods, you’ll also examine several beginning-to-end tidy text analyses on data sources from Twitter to NASA datasets. These analyses bring together multiple text mining approaches covered in the book.
Tackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools from the R ecosystem. You’ll discover how tidy data principles can make text mining easier, more effective, and consistent by employing tools already in wide use.
Get real-world examples for implementing text mining using tidy R package
Understand natural language processing concepts like sentiment analysis, tf-idf, and topic modeling
Learn how to analyze unstructured, text-heavy data using R language and ecosystem.

Is there someone you'd like to see at R User Day? Send a note to suggestions@datadaytexas.com. Would you like to speak at R User Day? Visit our proposals page.