Presented on April 28, 2021- Invited presentation at an internal FDA Data Science workshop

When You do Applied Statistics, You're Acting Like a Scientist. Why Does this matter? by Andrew Gelman Visit https://rstats.ai/nyr/ to learn more. Abstract: When you do applied statistics, you form hypotheses, gather data, run experiments, modify your theories, etc. Here, I'm not talking about hypotheses of the form "theta = 0" or whatever; I'm talking about hypotheses such as, "N=200 will be enough for this study" or "Instrumental variables should work on this problem" or "We can safely use the normal approximation here" or "We really need to include a measurement-error model here" or "The research question of interest is unanswerable from the data we have here; what we really need to do is . . .", etc. Existing treatments of statistical practice and workflow (including in my own textbooks) do not really capture the way that the steps of statistical design, data collection, analysis, and decision making feel like science. We discuss the implications of this perspective and how it can make us better statisticians and data scientists. Bio: Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina). Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics. Twitter: https://twitter.com/StatModeling Presented at the 2022 New York R Conference (June 9, 2022)

Combining Analysis Work with Reports and Presentations: What I Would Change About My Dissertation Visit https://rstats.ai/nyr/ to learn more. Abstract: Starting off with a good workflow baseline helps scale a project's complexity. Even the tasks of creating an RStudio Project and having a folder structure can go a long way in managing large and complex projects. I'll give an example of the things I've done in managing my dissertation (git, git submodules, github actions, and r project workflows), some of the corners I cut, and how upcoming tools (i.e., Quarto) can help round those corners. Bio: Daniel teaches data science at UBC and works as a data science educator for RStudio, working on the RStudio Academy team. He just moved to Vancouver *by car* in a cross-country across-border road trip with his dad. Twitter: https://twitter.com/chendaniely Presented at the 2022 New York R Conference (June 9, 2022)

Using R for GIS and Mapping by Jared Lander Visit https://rstats.ai/nyr/ to learn more. Abstract: Drawing maps is one of the world's oldest forms of data visualization. While maps are straightforward to interpret, they can be harder to make. Yet it can all be done inside R, in many different ways. We will make maps using {ggplot2}, {tmap} and {leaflet}, including very large maps using WebGL. We will also see some GIS operations including including determining which points fall inside which polygons and how to triangulate a point on the Earth based on known distances to other areas. Bio: Jared P. Lander is Chief Data Scientist of Lander Analytics, 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. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone, a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world. He is a member of the Strata New York selection committee. His writings on statistics can be found at jaredlander.com. Twitter: https://twitter.com/jaredlander Presented at the 2022 New York R Conference (June 9, 2022)

The Many Ways to Create a Composite Score in Sports & Beyond by Mike Band Visit https://rstats.ai/nyr/ to learn more. Bio: Mike Band is a data scientist architect at Lander Analytics and an analyst with the National Football League’s Next Gen Stats team. With a master’s degree in analytics from the University of Chicago, and bachelor’s degree from the University of Florida, Mike specializes in driving actionability through data science projects. Working both as a data scientist and on the business development side of Lander Analytics, Mike’s combination of programming skills, applied statistics background and communication abilities help drive data science projects from start to finish. Mike’s key to creating value through analytics: Effective communication of complex results into comprehensible, need-to-know reporting personalized for the intended audience. Twitter: https://twitter.com/MBandNFL Presented at the 2022 New York R Conference (June 9, 2022)

DEF CON 29 Aerospace Village Presentation: Don’t fear the BUS, it won’t run you over Discussion on DEF CON Discord server in #av-lounge-bar-text Presenter: Nicholas Childs https://www.aerospacevillage.org

Using {targets}, {arrow}, Docker, Postgres and the Command Line for Medium Data by Jared P. Lander Visit https://rstats.ai/gov/ to learn more. Abstract: Most companies don't have big data, but rather medium data, that awkward in between where the data are too big to fit in memory but not big enough for Google-scale systems. Fortunately R has many options for working with data of this size. We will look at using the command line, {data.table} and {dplyr} to clean the data and load it into a Postgres database inside a Docker container. Then we will use {targets} to orchestrate the whole process. Bio: Jared P. Lander is Chief Data Scientist of Lander Analytics, 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. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone, the best-selling book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world. His writings on statistics can be found at jaredlander.com. Twitter: https://twitter.com/jaredlander Presented at the 2021 Government & Public Sector R Conference (December 9, 2021)

Wrong Again! 30+ Years of Statistical Mistakes by Andrew Gelman Visit https://rstats.ai/nyr/ to learn more. Abstract: One of the benefits of a long career is that it gives us an opportunity to reflect upon all the ways our thinking has changed. In this talk I'll go over several places where my thinking has changed, for each considering why I previously took a stance that I currently disagree with, and where I anticipate my views might change further. I hope this discussion will be useful in helping each of you to introspect on your own past and future intellectual development. Bio: Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina). Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics. Twitter: https://twitter.com/StatModeling Presented at the 2021 New York R Conference (September10 , 2021)