The SAS Data Science Blog
Advanced analytics from SAS data scientists
Technological advancements in connectivity and global positioning systems (GPS) have led to increased data tracking and related business use cases to analyze such movements. Whether analyzing a vehicle, an animal or a population's movements - each use case requires analyzing underlying spatial information. Global challenges such as virus outbreaks, deforestation
This post, written by Radhikha Myeni and Jagruti Kanjia, will demonstrate how easy it is to build and deploy a machine learning pipeline by using SAS and Python. The Model Studio platform provides a quick and collaborative way to build complex pipelines by dragging and dropping nodes from a web-based
SAS System Engineer Sophia Rowland reveals how to embed decision flows into webpages and applications using the Microsoft Power Platform for a better customer experience.
SAS' Ghohui Wu shows you how to construct spatial weights matrices based on contiguity and distance measures, then shows how PROC CSPATIALREG automates spatial regression model selection.
SAS' Fijoy Vadakkumpadan, a computer vision expert, sheds light on how loadImages works in SAS Viya.
This is the second post in a series covering parallel processing in SAS Viya. The first post served as an introduction to parallel processing. It covered parallel processing uses in data science and the SAS Viya products that facilitate it. There are countless opportunities for using parallel processing within data