Category
18 course(s) found
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Data Mining
SAS - Survival Data Mining: A Programming Approach (Course)
FBMCE
This advanced course discusses predictive hazard modeling for customer history data. Designed for data analysts, the course uses SAS/STAT software to illustrate various survival data mining methods...
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Data Mining
Survival Data Mining Using SAS(R) Enterprise Miner(TM) (Course)
FBSDM
This advanced course covers predictive hazard modeling for customer history data. Designed for analysts, the course uses SAS Enterprise Miner to illustrate survival data mining methods and their...
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Data Mining
SAS(R) Enterprise Miner(TM) Integration with Open Source Languages (Course)
AEMOSI
This course introduces the basics for integrating R programming and Python scripts into SAS and SAS Enterprise Miner. Topics are presented in the context of data mining, which includes data...
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Data Mining
SAS - Data Mining Techniques: Theory and Practice (Course)
BDMT12
This course introduces a data mining methodology that is a superset to the SAS SEMMA methodology around which SAS Enterprise Miner is organized. The course also introduces a wide range of data mining...
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Data Mining
SAS - Data Mining: Principles and Best Practices (Course)
BELDR
Data mining is an advanced science that can be difficult to do correctly. This course introduces you to the power and potential of data mining and shows you how to discover useful patterns and trends...
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Data Mining
Customer Segmentation Using SAS(R) Enterprise Miner(TM) (Course)
BEMC41
No marketing strategy can be effective without segmentation. While the concept of segmentation is deceptively simple, in practice it is extremely difficult to execute. Emphasizing practical skills as...
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Data Mining
Development of Credit Scoring Applications Using SAS(R) Enterprise Miner(TM) (Course)
CSEM42
This course teaches students how to build a credit scorecard from start to finish using SAS Enterprise Miner 14.2 and the methodology recommended by leading credit and financial experts. The...
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Data Mining
SAS - Survival Data Mining: A Programming Approach (Course)
BMCE42
This advanced course discusses predictive hazard modeling for customer history data. Designed for data analysts, the course uses SAS/STAT software to illustrate various survival data mining methods...
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Data Mining
Advanced Predictive Modeling Using SAS(R) Enterprise Miner(TM) (Course)
PMA42
This course covers advanced topics using SAS Enterprise Miner including how to optimize the performance of predictive models beyond the basics. The course continues the development of predictive...
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Data Mining
SAS - Big Data, Data Mining, and Machine Learning (Course)
BDDM42
This course introduces the concepts of analytical computing and various data mining concepts, including predictive modeling, deep learning, and open source integration. The course introduces a wide...
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Data Mining
Supervised Machine Learning Procedures Using SAS(R) Viya(R) in SAS(R) Studio (Course)
DMML34
This course combines data exploration, visualization, data preparation, feature engineering, sampling and partitioning, model training, scoring, and assessment. It covers a variety of statistical,...
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Data Mining
SAS(R) AI & Machine Learning Certification Program (Course)
AIBT13
The SAS AI & Machine Learning Certification Program is designed for data professionals who want to learn artificial intelligence and machine learning techniques using SAS Viya. Students learn to...
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Data Mining
SAS(R) AI & Machine Learning Certification Program (AI only) (Course)
AIBT23
This program is designed for data professionals who want to earn a SAS Certified Professional: Artificial Intelligence and Machine Learning credential and have already passed one of the following...
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Data Mining
Machine Learning Using SAS(R) Viya(R) (Course)
CPML8I
This course covers the theoretical foundation for different techniques associated with supervised machine learning models. A series of demonstrations and exercises is used to reinforce all the...
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Data Mining
Deep Learning Using SAS(R) Software (Course)
DLUS3I
This course introduces the essential components of deep learning. Participants learn how to build deep feedforward, convolutional, and recurrent networks. The neural networks are used to solve...