SAS Enterprise Miner- Data Mining and Predictive Modeling

SAS Enterprise Miner: Data Mining and Predictive Modeling, available at $19.99, has an average rating of 4.75, with 61 lectures, based on 8 reviews, and has 4395 subscribers.

You will learn about Introduction to SAS Enterprise Miner and its capabilities for predictive modeling and data mining. Importing datasets in various formats such as text, CSV, xlsx, and xls. Understanding user operating concepts and software menus within SAS Enterprise Miner. Exploring statistical concepts like mean, standard deviation, and sample statistics. Performing variable selection using techniques like input variables, R-square values, and binary target variables. Combining different modeling techniques such as decision trees, neural networks, and regression models for enhanced predictive accuracy. Building and evaluating neural network models, including model weight history, ROC charts, and iteration plots. Implementing regression analysis with binary targets, interpreting regression model results, and creating effect plots. Engaging in practical exercises, case studies, and interactive discussions to reinforce learning. This course is ideal for individuals who are Data analysts and scientists seeking to deepen their understanding of advanced analytics tools. or Business intelligence professionals aiming to leverage predictive modeling for decision-making. or Students and academics interested in learning practical applications of statistical modeling. or Professionals in industries such as finance, healthcare, marketing, and retail looking to apply predictive analytics to their domain-specific datasets. or Anyone keen on mastering techniques like variable selection, neural networks, regression analysis, and decision trees using SAS Enterprise Miner. It is particularly useful for Data analysts and scientists seeking to deepen their understanding of advanced analytics tools. or Business intelligence professionals aiming to leverage predictive modeling for decision-making. or Students and academics interested in learning practical applications of statistical modeling. or Professionals in industries such as finance, healthcare, marketing, and retail looking to apply predictive analytics to their domain-specific datasets. or Anyone keen on mastering techniques like variable selection, neural networks, regression analysis, and decision trees using SAS Enterprise Miner.

Enroll now: SAS Enterprise Miner: Data Mining and Predictive Modeling

Summary

Title: SAS Enterprise Miner: Data Mining and Predictive Modeling

Price: $19.99

Average Rating: 4.75

Number of Lectures: 61

Number of Published Lectures: 61

Number of Curriculum Items: 61

Number of Published Curriculum Objects: 61

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Introduction to SAS Enterprise Miner and its capabilities for predictive modeling and data mining.

Importing datasets in various formats such as text, CSV, xlsx, and xls.

Understanding user operating concepts and software menus within SAS Enterprise Miner.

Exploring statistical concepts like mean, standard deviation, and sample statistics.

Performing variable selection using techniques like input variables, R-square values, and binary target variables.

Combining different modeling techniques such as decision trees, neural networks, and regression models for enhanced predictive accuracy.

Building and evaluating neural network models, including model weight history, ROC charts, and iteration plots.

Implementing regression analysis with binary targets, interpreting regression model results, and creating effect plots.

Engaging in practical exercises, case studies, and interactive discussions to reinforce learning.

Who Should Attend

Data analysts and scientists seeking to deepen their understanding of advanced analytics tools.

Business intelligence professionals aiming to leverage predictive modeling for decision-making.

Students and academics interested in learning practical applications of statistical modeling.

Professionals in industries such as finance, healthcare, marketing, and retail looking to apply predictive analytics to their domain-specific datasets.

Anyone keen on mastering techniques like variable selection, neural networks, regression analysis, and decision trees using SAS Enterprise Miner.

Target Audiences

Data analysts and scientists seeking to deepen their understanding of advanced analytics tools.

Business intelligence professionals aiming to leverage predictive modeling for decision-making.

Students and academics interested in learning practical applications of statistical modeling.

Professionals in industries such as finance, healthcare, marketing, and retail looking to apply predictive analytics to their domain-specific datasets.

Anyone keen on mastering techniques like variable selection, neural networks, regression analysis, and decision trees using SAS Enterprise Miner.

Welcome to our course on SAS Enterprise Miner! In this comprehensive program, you will delve into the intricacies of predictive modeling and data mining using one of the industry’s leading tools, SAS Enterprise Miner. Throughout this course, you will learn how to leverage the powerful features of SAS Enterprise Miner to extract meaningful insights from your data, build robust predictive models, and make informed business decisions. Whether you’re a seasoned data analyst or a beginner in the field, this course will equip you with the skills and knowledge needed to excel in the world of data science and analytics using SAS Enterprise Miner. Join us on this exciting journey as we explore the vast capabilities of SAS Enterprise Miner and unlock the potential of your data!

Section 1: SAS Enterprise Miner Intro

In this section, you’ll receive a comprehensive introduction to SAS Enterprise Miner, a powerful tool for predictive modeling and data mining. Starting with the basics, you’ll learn how to navigate the interface, select datasets, and create input data nodes. Through hands-on demonstrations, you’ll explore various features such as metadata advisor options, sample statistics, and trial reports, laying a strong foundation for your journey ahead.

Section 2: SAS Enterprise Miner Variable Selection

This section focuses on variable selection techniques in SAS Enterprise Miner. You’ll delve into concepts like input variables, R-square values, and binary target variables. Through practical exercises, you’ll gain insights into variable selection methods, frequency tables, and model comparison. By the end of this section, you’ll be equipped with the skills to effectively choose and analyze variables for your predictive models.

Section 3: SAS Enterprise Miner Combination

In this section, you’ll learn how to combine different models in SAS Enterprise Miner to enhance predictive accuracy. You’ll explore techniques like decision trees, neural networks, and regression models. Through interactive sessions, you’ll understand model iteration plots, subseries plots, and ensemble diagrams. By the end of this section, you’ll be proficient in combining and analyzing diverse modeling techniques for optimal results.

Section 4: SAS Enterprise Miner Neural Network

This section delves into neural network modeling using SAS Enterprise Miner. You’ll learn about neural network architectures, model weight history, and ROC charts. Through practical examples, you’ll gain hands-on experience in building and evaluating neural network models. By mastering neural network techniques, you’ll be able to tackle complex data mining tasks and extract valuable insights from your data.

Section 5: SAS Enterprise Miner Regression

In this final section, you’ll explore regression modeling techniques in SAS Enterprise Miner. You’ll learn how to perform regression analysis with binary targets, interpret regression model results, and create effect plots. Through step-by-step tutorials, you’ll understand the intricacies of regression modeling and its applications in predictive analytics. By the end of this section, you’ll have a solid understanding of regression techniques and their role in data-driven decision-making.

Throughout the course, you’ll engage in practical exercises, real-world case studies, and interactive discussions to reinforce your learning. Whether you’re a novice or an experienced data scientist, this course will empower you to harness the full potential of SAS Enterprise Miner for predictive modeling and data analysis.

Course Curriculum

Chapter 1: SAS Enterprise Miner Intro

Lecture 1: Introduction of SAS Enterprise Miner

Lecture 2: Select a SAS Table

Lecture 3: Creating Input Data Node

Lecture 4: Metadata Advisor Options

Lecture 5: Add More Data Sources

Lecture 6: Sample Statistics

Lecture 7: Trial report

Lecture 8: Properties of Cluster Node

Lecture 9: Variable Selection

Chapter 2: SAS Enterprise Miner VARIABLE SELECTION

Lecture 1: Input Variable

Lecture 2: Input Variable Continues

Lecture 3: Values of R-Square

Lecture 4: More on Variable Selection

Lecture 5: Binary Target Variable

Lecture 6: Variable and Effect Summary

Lecture 7: Variable Selection – Variable IDs

Lecture 8: Variable Frequency Table

Lecture 9: Variable S – Updating Model Comparison

Lecture 10: Run Data Partition Node

Lecture 11: Variable Selection – Fit Statistics

Lecture 12: Understanding Transformation of Variables

Lecture 13: Score Ranking Overlay Res

Lecture 14: Update Transformation of Variables

Chapter 3: SAS Enterprise Miner COMBINATION

Lecture 1: Combination of Different Models

Lecture 2: Properties of Neural Network

Lecture 3: Analyzing the Output Variable

Lecture 4: Combination of Regression Model

Lecture 5: Combination – Result of Regression Node

Lecture 6: Combination Iteration Plot

Lecture 7: Subseries Plot

Lecture 8: Creating Densemble Diagram

Lecture 9: SAS Code

Lecture 10: Decision Tree Model

Lecture 11: Run and Upadate Decision Tree Model

Lecture 12: Creating Dscore Node

Lecture 13: DT – Resulf of Model Comparison

Lecture 14: Leaf Statistics and Tree Map

Lecture 15: Interactively Decision Trees

Lecture 16: Result Node Data Partition

Lecture 17: Interactively Trees Window

Lecture 18: Building a Decision Trees

Chapter 4: SAS Enterprise Miner NEURAL NETWORK

Lecture 1: Neural Network Model

Lecture 2: Neural Network Model Output

Lecture 3: Model Weight History

Lecture 4: Neural Network – Final Weight

Lecture 5: ROC Chart

Lecture 6: Neural Network -Iteration Plot

Lecture 7: Neural Network – SAS Code

Lecture 8: Neural Network – Cumulative Lift

Lecture 9: Decision Processing

Lecture 10: Results of Auto Neural Node

Lecture 11: Run Model Comparison

Lecture 12: DEX – Variable IDs

Lecture 13: Average Square Error

Lecture 14: Score Rating overlay – Event

Lecture 15: Run Dmine Regression Node

Chapter 5: SAS Enterprise Miner REGRESSION

Lecture 1: Regression with Binary Target

Lecture 2: Regression – Table Effect Plots

Lecture 3: Result of Regression Model

Lecture 4: Update Regression Node

Lecture 5: Creating Flow Diagram

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

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