satRday Los Angeles 2019
| title | author | length |
|---|---|---|
| Opening Remark | Julie A. Sina, Amy Chen | 06m |
Opening remarks state the conference is hosted at the UCLA James West Alumni Center in partnership with the UCLA Alumni Association, lists sponsors and student… | ||
| Future: Friendly Parallel and Distributed Processing in R | Henrik Bengtsson | 44m |
Claims a single, portable R API lets code be written once and run locally, on clusters or in the cloud using the CRAN packages future and future.apply (plus… | ||
| From correlation to causation: measuring ad effectiveness at scale | Tamara Greasby | 46m |
Describes a production pipeline that creates randomized holdouts inside a DSP to measure causal ad lift, logging bids to S3, aggregating with Spark/Scala… | ||
| Lifting the Curtain on Machine Learning | Norm Matloff | 50m |
Two R packages, polyreg and prVis, implement the core claim that many neural nets are effectively high‑degree polynomial regression: polyreg generates all… | ||
| Scalable Automatic Machine Learning with H2O | Erin Ledell | 44m |
H2O AutoML runs a distributed Java cluster accessed from R/Python to train many models in parallel using random grid search over GBMs, XGBoost, GLMs, random… | ||
| Using Data Science to solve Platform Problems at TrueCar | Rachel Wang | 40m |
TrueCar buys a transaction feed (about 70 million transactions at ~$8–10M/year) and exposes a market curve that drives ~100 million unique visitors annually… | ||
| Better Research Planning Through Simulation | Neal Fultz | 35m |
Shows a concrete workflow: use fabricatr, randomizr and estimatr inside DeclareDesign to declare a population, sampling and assignment, and an estimator, then… | ||
| A Tidy Analysis of LAX Holiday Traffic | Greg Botwin | 05m |
Hourly tweets from @flyLAXairport between 2018-12-19 and 2019-01-02 are parsed with rtweet and converted to tidy format with tidyverse tools, extracting times… | ||
| Performing Data Science: A Musician's Perspective | Leondra James | 24m |
Argues that liberal arts and performing-arts backgrounds map to the domain-expertise circle of data science and that the barrier is practice not innate talent… | ||
| R for teaching ecology | Gaurav Kandlikar | 05m |
Undergraduate students use small Shiny apps built around the de salt differential‑equation solver to tweak parameters in Lotka‑Volterra models and see concrete… | ||
| Shiny: A GUI for the Tidyverse | Carl Ganz | 27m |
Argues that Shiny is the next abstraction layer above the tidyverse, turning decades of R functionality into browser-accessible R tools by making front end… | ||
| Physical Culture and Working Memory | Michael Espero | 05m |
Small, underpowered experiment compares 30 minutes Seinfeld vs treadmill (self‑paced moderate intensity) and finds the treadmill group has a higher median… | ||
| Integrating R & Google Drive or Collaborative Research | Hannah Rempel | 05m |
Workflow uses Google Drive, Google Docs and Google Sheets plus the googlesheets R package to read and write sheets by key so no intermediate CSVs are saved… | ||
| How R-Maps Showed an Old Industry a New Route | Yulia Guan | 05m |
Economic consultants introduce R maps to an industry that relies on SAS and Stata, build interactive maps plotting store locations and draw 0.5 mile radius… | ||
| Computing info criteria multilevel models using diff sample sizes | Wendy C | 05m |
Shows how to extract logLik from nlme output and recompute information criteria so BIC uses clusters instead of observations, using str() to find logLik and… | ||
| Pipes or Brackets: Dplyr and Data.Table | Jeremy Guinta, Amy Linehan | 32m |
Recreates payroll timekeeping from six messy sources into a unified table of 102 million rows and shows concrete timings: pure data.table loads and aggregates… | ||
| Closing Remark | Linda Suen | 01m |
Organizers thank attendees, sponsors, and local supporters and note SatRday began in June 2016 in Budapest and has grown to over ten meetups worldwide. Drinks… | ||
| Productionizing R scripts in the cloud | Gergely Daroczi | 29m |
Checklist for taking an R prototype to production: use git and code linters, pack reusable helpers into an internal R package, and enforce style via… | ||
| nothing matches | ||