Workshops: July 11-12 | Location: TBA
Conference: July 13-14 | Location: FIAF Manhattan
Speakers
Max Kuhn
Scientist
Posit
Talk: Using Tidymodels to Calibrate Regression in Classification Models
David Robinson
Principal Data Scientist
Heap
Talk: The Science of Product Development: Bringing Causal Inference to Conversion and Retention Metrics
More speakers coming soon!
Workshops
Tidy Time Series and Forecasting in R
Hosted by Rob Hyndman
Tue, Jul 11 - Wed, Jul 12 | 9:00am - 5:00pm
It is common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always suitable to handle the scale, frequency and structure of the data collected. In this workshop, we will look at some packages and methods that have been developed to handle the analysis of large collections of time series. On day 1, we will look at the tsibble data struc...
...cture for flexibly managing collections of related time series. We will look at how to do data wrangling, data visualizations and exploratory data analysis. We will explore feature-based methods to explore time series data in high dimensions. A similar feature-based approach can be used to identify anomalous time series within a collection of time series, or to cluster or classify time series. Primary packages for day 1 will be tsibble, lubridate and feasts (along with the tidyverse of course). Day 2 will be about forecasting. We will look at some classical time series models and how they are automated in the fable package, and we will explore the creation of ensemble forecasts and hybrid forecasts. Best practices for evaluating forecast accuracy will also be covered. Finally, we will look at forecast reconciliation, allowing millions of time series to be forecast in a relatively short time while accounting for constraints on how the series are related. (In-Person Only)
Machine Learning in R
Hosted by Max Kuhn
Tue, Jul 11 - Wed, Jul 12 | 9:00am - 5:00pm
Join Max Kuhn on a tour through Machine Learning in R, with emphasis on using the software as opposed to general explanations of model building. This workshop is an abbreviated introduction to the tidymodels framework for modeling. You'll learn about data preparation, model fitting, model assessment and predictions. The focus will be on data splitting and resampling, data pre-processing and featur...
...re engineering, model creation, evaluation, and tuning. This is not a deep learning course and will focus on tabular data. Pre-requisites: some experience with modeling in R and the tidyverse (don't need to be experts); prior experience with lm is enough to get started and learn advanced modeling techniques. In case participants can’t install the packages on their machines, RStudio Server Pro instances will be available that are pre-loaded with the appropriate packages and GitHub repository. (In-Person & Virtual)
Bayesian Data Analysis and Stan
Hosted by Jonah Gabry
Tue, Jul 11 - Wed, Jul 12 | 9:00am - 5:00pm
This workshop will introduce the basics of applied Bayesian data analysis, the Stan modeling language, and how to interface with Stan from R. Participants will learn to write their own models in the Stan language, run them in R, and use a variety of R packages to work with the results. (In-Person & Virtual)
Causal Inference in R
Hosted by Malcolm Barrett & Lucy D'Agostino McGowan
Tue, Jul 11 - Wed, Jul 12 | 9:00am - 5:00pm
In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elem...
...ments of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know--the tidyverse, regression models, and more--to answer the questions that are important to your work. This course is for you if you: -Know how to fit a linear regression model in R -Have a basic understanding of data manipulation and visualization using tidyverse tools -Are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships (In-Person & Virtual)
More workshops coming soon!