The IEEE Visual Analytics Science and Technology (VAST) Challenge provides a great opportunity to validate our software against real-world scenarios using complex data sets. Not only do we learn from these projects, but we also send feedback to our development teams to further improve product capabilities for customers.
The Proc Python procedure, Python code editor & Python code step facilitate low-code analytics calling Python and SAS from a common interface. Data scientists also appreciate the connection to Python & R through the Model Studio Open-source Code node. Older methods of interaction include the swat and sas_kernel packages running on Python clients.
Data visualization is a critical way for anyone to turn endless rows of data into easy-to-understand results through dynamic and understandable visuals. Whether your favorite visualization is a pie chart, a geographic map or relies on natural language, showing the insights that empower you to make more informed decisions is a better way to do data-driven business. Analyst firms say that SAS has market-leading data visualization. This helps users across the globe find insights in their data using new and exciting trends in data visualization.
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
Through hyperparameter autotuning, you can maximize a model's performance without maximizing effort. While SAS searches the hyperparameter space in the background, you are free to pursue other work.
Did you know that you can use SAS Enterprise Miner 15.1 to easily create an ASTORE? You can create an ASTORE from an HP SVM node, an HP FOREST node, and also from some SAS Viya Code Nodes! This blog will show you how.
This blog is a part of a series on the Data Science Pilot Action Set. In this blog, we discuss updates to Visual Data Mining and Machine Learning with the release of Viya 3.5. In the middle of my blog series, SAS released Viya 3.5. Included in Viya 3.5 was the
This blog focuses on using SASPy for modeling and machine learning.
SAS Viya is a cloud-enabled, in-memory analytics engine which allows for rapid analytics insights. Viya utilizes the SAS Cloud Analytics Services (CAS) to perform various actions and tasks. Best of all, CAS is accessible from various interfaces including R. In this blog, I will go through a few blocks one of my notebooks, which moves through an analytics workflow using R and SAS.
Validating and testing our supervised machine learning models is essential to ensuring that they generalize well. SAS Viya makes it easy to train, validate, and test our machine learning models.
