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SAS

Podnikové informační systémy

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SAS(R) Enterprise Miner(TM) Integration with Open Source Languages

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Mixed Models Analyses Using SAS(R)

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Anti Financial Crime – Applied Analytics Training

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Analytics Value Training for Insurance

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Credit Risk Modeling

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Categorical Data Analysis Using Logistic Regression

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SAS(R) Visual Analytics 2 for SAS(R) Viya(R): Advanced

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Applied Analytics Using SAS(R) Enterprise Miner(TM)

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Advanced Machine Learning Using SAS(R) Viya(R)

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Graphing Data Effectively and Avoiding Common Pitfalls

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Advanced Analytics for IoT Using SAS(R) Event Stream Processing

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SAS(R) Anti-Money Laundering in SAS(R) Viya(R): Administration

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SAS(R) Anti-Money Laundering in SAS(R) Viya(R) Overview

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Retail Capability Academy

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Essential Functions of SAS(R) Intelligent Decisioning

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Fraud Detection Using Descriptive, Predictive, and Social Network Analytics

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Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS(R)

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Survival Data Mining: A Programming Approach

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High-Performance Data Processing with CASL in SAS(R) Viya(R)

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SAS(R) Customer Due Diligence in SAS(R) Viya(R) Administration

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SAS(R) Customer Due Diligence in SAS(R) Viya(R) Overview

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> SAS(R) Event Stream Processing Essentials SAS(R) Event Stream Processing Essentials

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Introduction to SAS/ACCESS(R) Interface to Teradata

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Introduction to SAS(R) and Hadoop

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SAS(R) Federation Server: Accessing and Virtualizing Enterprise Data

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Hadoop Data Management with Hive, Pig, and SAS(R)

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Working with SAS(R) Data Loader for Hadoop

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Deep Learning Using SAS(R) Software

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Feature Engineering and Data Preparation for Analytics

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Supervised Machine Learning Procedures Using SAS(R) Viya(R) in SAS(R) Studio

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Neural Network Modeling

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Understanding the SAS(R) Quality Knowledge Base

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Creating a New Data Type in the SAS(R) Quality Knowledge Base

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Using DataFlux(R) Data Management Studio

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System Tuning Using SAS(R) Enterprise Session Monitor

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Efficiency Tips for Database Programming in SAS(R)

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SAS(R) Fraud Management Overview

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Forecasting Using SAS(R) Forecast Server Software

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Forecasting Using Model Studio in SAS(R) Viya(R)

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SAS/GRAPH(R): Essentials

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Programming with SAS/IML(R) Software

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Neural Networks: Essentials

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Longitudinal Data Analysis Using Discrete and Continuous Responses

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Optimization Concepts for Data Science

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Optimization Concepts for Data Science and Artificial Intelligence

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SAS(R) Grid Manager: Administration

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SAS(R) Programming 2: Data Manipulation Techniques

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SAS(R) Programming 3: Advanced Techniques

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Using SAS(R) Model Risk Management

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SAS(R) Risk Stratum for CECL: Workshop

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