Founded by Josh Reich and Drew Conway, the New York Open Statistical Programming Meetup started as the New York R Meetup with a handful of people in an office at Union Square Ventures. Since then it has grown to over 8,000 members and has been hosted at NYU, Columbia, AOL, iHeartRadio, eBay, Work-Bench and other locations.

Our mission is to spread knowledge of statistical programming techniques in open-source languages such as R, Python, Julia and Go, and data science in general. Another important aspect is community building and socializing. The meetups start with pizza, followed by a 45-90 minute talk, ending with a trip to the local bar.

Attending

To attend please visit the meetup page.

Presentations and Videos

Whenever possible we make presentations available at the Presentations page.

We now stream and host videos of meetups on Facebook and YouTube and older videos are scattered on a variety of services. They are also listed on the Presentations page.

Jobs

Job openings and other announcements are on the meetup discussion board.

Upcoming Meetup

BART for Causal Inference: How combining machine learning and statistics makes l

March 8, 2018 06:30:00 PM

Our March meetup features Jennifer Hill, co-author of Data Analysis Using Regression and Multilevel/Hierarchical Models and Professor at NYU.

Thank you to eBay NYC for hosting us.

We have a number of other events planned for the next few months, including office hours, master class and the 2018 New York R Conference. Members can use code nyhackr for a 20% discount.

About the Talk:

There has been increasing interest in the past decade in the use of machine learning tools in causal inference to help reduce reliance on parametric assumptions and allow for more accurate estimation of heterogeneous effects.  This talk reviews the work in this area that capitalizes on Bayesian Additive Regression Trees, an algorithm that embeds a tree-based machine learning technique within a Bayesian framework to allow for flexible estimation and valid assessments of uncertainty.  It will further describe extensions of the original work to address common issues in causal inference:  lack of common support, violations of the ignorability assumption, and generalizability of results to broader populations.  It will also describe existing R packages for traditional BART implementation as well as debut a new R package for causal inference using BART, bartCause.


About Jennifer:

Jennifer Hill works on development of methods that help us to answer the causal questions that are so vital to policy research and scientific development. In particular, she focuses on situations in which it is difficult or impossible to perform traditional randomized experiments, or when even seemingly pristine study designs are complicated by missing data or hierarchically structured data. Most recently Hill has been pursuing two major strands of research. The first focuses on Bayesian nonparametric methods that allow for flexible estimation of causal models without the need for methods such as propensity score matching. The second line of work pursues strategies for exploring the impact of violations of typical assumptions in this work that require that all confounders have been measured. Hill has published in a variety of leading journals including Journal of the American Statistical Association, Annals of Applied Statistics, American Political Science Review, American Journal of Public Health, Political Analysis, and Developmental Psychology. Hill earned her PhD in Statistics at Harvard University in 2000 and completed a post-doctoral fellowship in Child and Family Policy at Columbia University's School of Social Work in 2002.


Pizza (https://nyhackr.org/pizzapoll.html) begins at 6:30, the talk starts at 7, then after we head to the local bar.

Website

The nyhackr website was built as a RMarkdown website and the source code can accessed by the community on GitHub.

How to contribute

If you wish to contribute to the website the process is pretty simple.

  1. Fork and clone the repository (an example can be found here)
  2. Create a new branch for your changes (warning, this step cannot be done in RStudio!)
  3. Make your changes. You can build and view your local version by using rmarkdown::render_site()
  4. When you are done, submit a pull request. Your changes might not appear on the public site right away as we have a development version for making sure changes don't break the site.