This demonstration was aimed at introducing SAS, features of SAS Enterprise Miner and how to use SAS EMiner to build a prediction model. Presented for a group of masters student at Brunel University.

1. An Introduction to
SAS Enterprise Miner
CS5608: Big Data Analytics
By: Yasoda Jayaweera
Brunel University, London

2. OUTLINE
▪ An Introduction to SAS
▪ Importance of SAS
▪ Demo
▪ Building a decision tree with SAS Enterprise Miner
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3. AN INTRODUCTION

4. SAS: A HISTORY
▪ Statistical Analysis System
▪ Began at North Carolina State University, US as a
project to analyze agricultural research
▪ A US based company founded in 1976
▪ Proprietary software
▪ SAS Base is the main software
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5. SAS IDEs
▪ SAS Studio
▪ SAS Enterprise Guide
▪ Use to write and run SAS code
▪ General purpose reporting and analysis (manipulate data,
describe data, graph data, and perform advanced statistical
analysis)
▪ SAS Enterprise Miner
▪ Specifically for predictive and descriptive modeling
▪ Interface for data mining/neural networks
▪ Used for specific data mining techniques to create statistical
models, scoring models, segmenting data and etc.
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6. IMPORTANCE OF SAS

7. IS SAS AN IMPORTANT PLAYER?
▪ Tradition/legacy
▪ Existing infrastructure (since 1976)
▪ Cost of transition
▪ Distrust of free software
▪ Lower processing times with Big Data
▪ Ability for sequential processing
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8. IS SAS AN IMPORTANT PLAYER?
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SAS Annual Report

9. IS SAS AN IMPORTANT PLAYER?
▪ Integrates with other proprietary software well
▪ Procedures are very well documented and
standardize coding
▪ Single-source support
▪ Many data scientists are not programmers and don't
care about using a cool language
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10. GARTNER’S MAGIC QUADRANT 2017
Gartner has recognised SAS as a “Leader” in the magic
quadrant for data science platforms
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11. SAS, R or PYTHON
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Burtch Works survey 2017

12. SAS CERTIFICATION
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▪ Global certifications
▪ Certification path

13. SAS APPLICATIONS

14. LEARNING MATERIAL
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15. SAS ENTERPRISE MINER
▪ Introduction to the SAS EMiner interface
▪ SAS SEMMA process
▪ Building a decision tree
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16. SAS SEMMA PROCESS
▪ Methodical approach that describes how an analysis
is performed
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Sample
Explore
ModifyModel
Assess

17. DATA
▪ The DONORS_RAW_DATA data set contains details of
donations from a previous mail solicitation campaign
at a charitable organization
▪ Mailing a solicitation is associated with a cost
▪ Mailed and responded - $15.00 (received donation on average)
▪ Mailed but no response - $0.50 (postage)
▪ Did not mail – no cost
▪ Target variable
▪ 1 - decision to mail a solicitation to an individual
▪ 0 - decision to not mail a solicitation
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18. SPEAKER
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Yasoda Jayaweera
PhD Student
Brunel University, London
(yasoda.jayaweera@brunel.ac.uk)