Emily Riederer

Photo credit C D-X on Unsplash A deeply relatable aspect of agentic coding is their use of progressive disclosure in skills and references.

Quick Links Test Set Episode Website Apple Podcasts | Spotify | YouTube Episode Notes Emily’s had a wild ride through modeling, data engineering, machine learning, and back again, and she knows a thing or three about the evolution of SQL tooling (from nig…

In October, the Python Software Foundation announced that it had made the difficult decision to forgo a $1.5M National Science Foundation grant.

Photo credit to Hans-Jurgen Mager on Unsplash polars API is a delight in part because of its consistency.

So, you build a great predictive model.

Data science stakeholder communication is hard.

Posit’s recently-announced project orbital translates fitted SciKitLearn pipelines to SQL for easy prediction scoring at scale.

Quick Links Podcast Episode Casual Inference is a podcast on all things epidemiology, statistics, data science, causal inference, and public health.

Quick Links Abstract Slides Slides This talk was part of a symposium on data science tools and opportunities for adoption in epidemiology.

Photo credit to the inimitable Allison Horst About a year ago, I wrote the original version of Python Rgonomics to help fellow former R users who were entering into the world of python.

Literate programming tools like R Markdown and Quarto make it easy to convert analyses into aesthetic documents, dashbaords, and websites for public sharing.

Quick Links Abstract Slides Video Slides Video Post - Python Rgonomics Post - Advanced polars versus dplyr Warning Tooling changes quickly.

Credible documentation is the best tool for working with data.

Photo credit to David Clode on Unsplash In the past few weeks, I’ve been writing about a stack of tools and specific packages like polars that may help R users feel “at home” when working in python due to similiar ergonomics.

We’ve all worked with poorly documented dataset, and we all know it isn’t pretty.

Photo credit to Hans-Jurgen Mager on Unsplash A few weeks ago, I shared some recommended modern python tools and libraries that I believe have the most similar ergonomics for R (specifically tidyverse) converts.

Documentation can be a make-or-break for the success of a data initiative, but it’s too often considered an optional nice-to-have.

Photo credit to the inimitable Allison Horst Warning Some advice in this post has gone stale regarding IDEs, installers, and environment management tools.

Last week, I enjoyed attending parts of the annual virtual Causal Data Science Meeting organized by researchers from Maastricht University, Netherlands, and Copenhagen Business School, Denmark.

Abstract In October, I joined a Halloween-themed panel along with Chad Sanderson and Joe Reis to discuss our horror stories of data quality gone wrong and how to build successful data quality strategies in large organizations.