This workshop was organized by "Orlando Machine Learning and Data Science Meetup". (Happened on 2019-02-23 between 15:00 and 17:00.)

The goals of this workshop are to introduce the theoretical background of Quantile Regression (QR), to demonstrate QR's practical (and superior) abilities to deal with "real life" time series data, and to teach how to rapidly create QR workflows using Mathematica or R.

The workshop has a dedicated GitHub project; see: https://github.com/antononcube/MathematicaVsR/tree/master/Projects/QuantileRegressionWorkflows .

Here is an example workflow with a Mathematica pipeline:

qrmon =
   QRMonUnit[distData]?
   QRMonEchoDataSummary?
   QRMonQuantileRegression[12]?
   QRMonPlot;

Here is an example workflow with an R pipeline:

devtools::install_github("antononcube/QRMon-R")
library(QRMon)

qrmon <-
     QRMonUnit( dfTemperatureData ) %>%
     QRMonEchoDataSummary() %>%
     QRMonQuantileRegression( df = 16, degree = 3, quantiles = seq(0.1,0.9,0.2) ) %>%
     QRMonPlot( datePlotQ = TRUE, dateOrigin = "1900-01-01" )

Movies

Movies of the presentation/workshop:

https://www.youtube.com/watch?v=PP2Ib6yx560 (Part 1);

https://www.youtube.com/watch?v=se819HqpiLE (Part 2).

Only 5 people out of 16 brought laptops; only one person attending the workshop had Mathematica installed. I did use Mathematica a lot though for both data and conceptual examples. Two hands-on examples were done with R; one with Mathematica.

References

[1] Anton Antonov, "A monad for Quantile Regression workflows", (2018), Wolfram Community.

[2] Anton Antonov, "Completing XKCD curve-fitting post with QRMon", (2018), Wolfram Community.