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Learn SAS
Terry Barham 0
Are SAS practice exams worth the cost?

A SAS practice exam can help you prepare for SAS certification. Practice exams are similar in difficulty, objectives, length, and design of the actual exam. While there's no guarantee that passing the practice exam will result in passing the actual exam, they can help you determine how prepared you are for an exam.

Data Visualization
Rick Wicklin 0
The 80-20 rule for blogs

You've probably heard about the "80-20 Rule," which describes many natural and manmade phenomena. This rule is sometimes called the "Pareto Principle" because it was discovered by Vilfredo Pareto (1848–1923) who used it to describe the unequal distribution of wealth. Specifically, in his study, 80% of the wealth was held

Advanced Analytics
Susan Kahler 0
How to build deep learning models with SAS

SAS® supports the creation of deep neural network models. Examples of these models include convolutional neural networks, recurrent neural networks, feedforward neural networks and autoencoder neural networks. Let’s examine in more detail how SAS creates deep learning models using SAS® Visual Data Mining and Machine Learning. Deep learning models with

Analytics | Data Visualization | Fraud & Security Intelligence
David Kennedy 0
5 things law enforcement should know about SAS Intelligence and Investigation Management

Last week at SAS Global Forum, SAS launched a new solution for law enforcement. Powered by SAS® Visual Investigator, SAS® Intelligence and Investigation Management helps agencies integrate information to uncover sophisticated criminal activity, make connections in real time, and enhance collaboration in investigations. Data and analytics can provide tremendous value

Advanced Analytics | Analytics
Patricia Neri 0
An introduction to SAS Visual Forecasting 8.2

This post is an introduction to SAS Visual Forecasting 8.2. We'll build a Visual Forecasting (VF) Pipeline, which is a process flow diagram whose nodes represent tasks in the VF Process. The objective is to show how to perform the full analytics life cycle with large volumes of data: from accessing data and assigning variable roles accurately, to building forecasting models, to select a champion model and overriding the system generated forecast.

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