3 data scientist jobs and how to land them
The question to ask is no longer, “Do you want to be a data scientist?” But rather, “What kind of data scientist do you want to be?”
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Intelligent Decisioning: Ensuring fairness in analytically-driven decision making
Attend this session during the SAS Explore event on Sept 27-29 or view the recording at your convenience. We will showcase the use of SAS Intelligent Decisioning, SAS Model Manager, and SAS Visual Analytics on the SAS Viya platform for a solution that helps mitigate inequitable credit decisions.
Using PROC DEEPCAUSAL to optimize revenue through policy evaluation
SAS' Gunce Walton introduces to you a new scoring capability, how it utilizes Deep Neural Networks (DNNs) and shares use cases with PROC DEEPCAUSAL.
Analyzing demographics and patterns-of-life using SAS Visual Analytics
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.
Identifying influential users in a social network to aid public health
SAS' Damian Herrick chronicles the refresh of a 2002 social-network analysis aimed at identifying influential peer educators among former drug users.
Creating a Multi-stage Computer Vision model to detect objects on high-resolution imagery
Let's create a Multi-stage Computer Vision model to detect objects on high-resolution imagery taken from an aerial view. The goal is to locate a dog and determine if he is wearing a scarf or not and what color the scarf is.
How to reveal new connections in a knowledge graph with link prediction
In this introduction to powerful knowledge graph tools, SAS' Brandon Reese shows you how they can predict disease similarity and compound similarity using an unsupervised approach.
Digital twin development: Why simulations are critical
SAS' Bahar Biller reveals how simulations enable KPI generation, risk quantification, risk management and more.
Reinforcement Learning (RL): How we removed hurdles to adoption
Wouldn’t it be cool if we establish a mechanism that provides more data scientists easy access to SAS Reinforcement Learning capabilities, from a centralized location and using a standardized approach?
The SAS Batting Lab: The model powering the analysis
SAS research statistician Ji Shen reveals how to train a machine to be a batting coach.