And that's a wrap for our spring edition of ESUG! 👏
What a great day of learning, knowledge exchange, and connecting with new and old friends. Here's what we had in store for everyone.
8:30am - 9:00am
Registration and Light Breakfast
9:00am - 9:05am
Welcome Remarks @dougd , ESUG President
9:05am - 9:15am
SAS Updates Alice Yuan, SAS Canada
A quick look at new developments in the world of SAS.
9:15am - 9:45am
A Snapshot of Quantile Regression Anamaria Savu, University of Alberta
Quantile regression is a type of regression that models the conditional quantiles of a response variable on predictor variables. Unlike ordinary least squares (OLS) regression, which models the conditional mean of the response variable given the predictor variables, quantile regression allows to model separately different quantiles (such as the median, quartiles, or any other percentile) of the response variable.
She will discuss the advantages of quantile regression through examples. At the same time I will demonstrate how PROC QUANTREG can be used to perform quantile regression.
9:45am - 10:15am
Tips & Tricks
A Forest of Decisions: Navigating Complexity with Random Forest using SAS 9.4 Anthony Wu, Government of Alberta
Random Forest is a powerful ensemble learning technique widely used in machine learning for classification and regression tasks This presentation will explore some practical implementations of Random Forest in SAS 9.4, showcasing its effectiveness in predictive analytics and model building across various domains.
10:15am - 10:40am
Networking Break
10:40am - 11:10am
Automated Processing of Web-Based Research Data Using Proc HTTP and Macro Variable Lists @rickwatts , Women and Children's Health Research Institute
Rick and team use a web based system called REDCap to collect medical research data from study participants. A single REDCap system houses many thousands of projects, each of which can be designed and modified by individual researchers. This is a very dynamic data environment with project level data structures continually changing. Any such change is reflected in data extracts resulting in frequent re-writes of existing SAS code.
To address this challenge Rick has written a large lump of ugly SAS code that uses PROC HTTP to extract project data via REDCap’s RESTFUL API. System metadata allows REDCap concepts to be mapped to SAS data types, variable names, labels and formats before data is extracted and processed into individual, study specific data sets.
11:10am - 11:55am
Integrating Synthetic Data Generation in Machine Learning Modeling Pipelines @BrettWujek & @Sundaresh , SAS
Synthetic data generation is increasingly important for augmenting existing data sets, mitigating imbalance in data sets with rare events and enabling data science tasks without having to share sensitive real data. In this presentation, we show how to use SAS® analytical capabilities to efficiently generate high-quality synthetic data. We demonstrate the use of SAS procedures, a REST API in Python, related pre-processing and post-processing steps, as well as assessment metrics.
11:55am - 12:00pm
Closing Remarks
If you have a presentation idea for our next meeting, please let us know. See you at the next one! 😃
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