Data Mining with Rattle

Data Mining with Rattle, available at $44.99, has an average rating of 4.4, with 82 lectures, based on 91 reviews, and has 1649 subscribers.

You will learn about Perform and support life-cycle data mining tasks and activities using the popular Data Miner ("Rattle") software suite. Understand the functionalities implicit in the data, explore, test, transform, cluster, associate, model, evaluate, and log tabs in the Data Miner ("Rattle") GUI software platform. Know how to explore, visualize, transform, and summarize data sets in Rattle. Know how to create advanced, interactive Ggobi visualizations of data. Know how to use, estimate and interpret: cluster analyses; association analyses mining rules; decision trees; random forests; boosting; and support vector machines using Rattle. This course is ideal for individuals who are Anyone interested in data mining seeking to master the use of a powerful popular contemporary (and no-cost) Data Mining software suite or Data analytics professionals seeking to augment their data mining skill sets with a popular and useful data mining package. or Undergraduate and graduate students seeking to attain in-demand data mining skills for data analysis/mining tasks to offer to prospective employers. It is particularly useful for Anyone interested in data mining seeking to master the use of a powerful popular contemporary (and no-cost) Data Mining software suite or Data analytics professionals seeking to augment their data mining skill sets with a popular and useful data mining package. or Undergraduate and graduate students seeking to attain in-demand data mining skills for data analysis/mining tasks to offer to prospective employers.

Enroll now: Data Mining with Rattle

Summary

Title: Data Mining with Rattle

Price: $44.99

Average Rating: 4.4

Number of Lectures: 82

Number of Published Lectures: 82

Number of Curriculum Items: 82

Number of Published Curriculum Objects: 82

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

Perform and support life-cycle data mining tasks and activities using the popular Data Miner ("Rattle") software suite.
Understand the functionalities implicit in the data, explore, test, transform, cluster, associate, model, evaluate, and log tabs in the Data Miner ("Rattle") GUI software platform.
Know how to explore, visualize, transform, and summarize data sets in Rattle.
Know how to create advanced, interactive Ggobi visualizations of data.
Know how to use, estimate and interpret: cluster analyses; association analyses mining rules; decision trees; random forests; boosting; and support vector machines using Rattle.

Who Should Attend

Anyone interested in data mining seeking to master the use of a powerful popular contemporary (and no-cost) Data Mining software suite
Data analytics professionals seeking to augment their data mining skill sets with a popular and useful data mining package.
Undergraduate and graduate students seeking to attain in-demand data mining skills for data analysis/mining tasks to offer to prospective employers.

Target Audiences

Anyone interested in data mining seeking to master the use of a powerful popular contemporary (and no-cost) Data Mining software suite
Data analytics professionals seeking to augment their data mining skill sets with a popular and useful data mining package.
Undergraduate and graduate students seeking to attain in-demand data mining skills for data analysis/mining tasks to offer to prospective employers.

Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the “hands-on” use of a popular, contemporary data mining software tool, “Data Miner,” also known as the ‘Rattle’ package in R software. Rattle is a popular GUI-based software tool which ‘fits on top of’ R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a ‘cradle-to-grave’ data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package. The course is ideal for undergraduate students seeking to master additional ‘in-demand’ analytical job skills to offer a prospective employer. The course is also suitable for graduate students seeking to learn a variety of techniques useful to analyze research data. Finally, the course is useful for practicing quantitative analysis professionals who seek to acquire and master a wider set of useful job skills and knowledge. The course topics are scheduled in 10 distinct topics, each of which should be the focus of study for a course participant in a separate week per section topic.

Course Curriculum

Chapter 1: Introduction, Orientation, and Demos

Lecture 1: Course Overview

Lecture 2: Class Agenda and Introduction to Data Mining

Lecture 3: Explanation of Class Materials

Lecture 4: Demonstrations of Rattle

Lecture 5: More Rattle Demonstrations

Lecture 6: Exercise for Introduction Section

Chapter 2: Rattle Interface Tabs and Introductory Script Demonstrations

Lecture 1: Session Agenda

Lecture 2: Rattle Interface and Tabs (part 1)

Lecture 3: Rattle Interface and Tabs (part 2)

Lecture 4: Rattle Interface and Tabs (part 3)

Lecture 5: Script Demonstrations (part 1)

Lecture 6: Script Demonstrations (part 2)

Lecture 7: Script Demonstrations (part 3)

Chapter 3: Loading and Exploring Data

Lecture 1: Loading and Describing Data in Rattle

Lecture 2: Describing and Exploring Data in Rattle

Lecture 3: Exploring the Data in Rattle

Lecture 4: Exploring Data with Plots in Rattle

Lecture 5: Script to Load Data and Read Files

Lecture 6: More Data Visualization with Scripts

Lecture 7: Continue Plotting with Scripts

Chapter 4: Data Visualizations with Ggobi and Data Transformation in Rattle

Lecture 1: Interactive Data Exploration

Lecture 2: Ggobi Demonstrations

Lecture 3: Data Transformation in Rattle (part 1)

Lecture 4: Data Transformation in Rattle (part 2)

Lecture 5: Reshaping Data

Chapter 5: Cluster Analysis

Lecture 1: Introduction to Cluster Analysis using Rattle

Lecture 2: Similarity-based Cluster Analysis Demos using Scripts (part 1)

Lecture 3: Distance-based Cluster Analysis Demos using Scripts (part 2)

Lecture 4: Data Exploration Options (part 1)

Lecture 5: Data Exploration Options (part 2)

Lecture 6: Cluster Analysis Example: Ancient Pottery Shards

Lecture 7: Cluster Analysis Example: Classifying Exoplanets (part 1)

Lecture 8: Cluster Analysis Example: Classifying Exoplanets (part 2)

Chapter 6: Association Analysis

Lecture 1: Cluster Analysis Exercise Solution

Lecture 2: Introduction to Association Analysis

Lecture 3: Introduction to Association Analysis using R Script

Lecture 4: Introduction to Association Analysis using Rattle

Lecture 5: Working with Rules

Lecture 6: Visualizing Association Rules (part 1)

Lecture 7: Visualizing Association Rules (part 2)

Lecture 8: Visualizing Association Rules (part 3)

Lecture 9: Association Analysis Exercise

Chapter 7: Decision Trees and Recursive Partitioning

Lecture 1: Association Analysis Exercise Solution

Lecture 2: What are Decision Trees ?

Lecture 3: Introduction to Decision Trees and Rattle Demo (part 1)

Lecture 4: Introduction to Decision Trees and Rattle Demo (part 2)

Lecture 5: Introduction to Decision Trees and Rattle Demo (part 3)

Lecture 6: Introduction to Decision Trees and Rattle Demo (part 4)

Lecture 7: Recursive Partitioning Demo with Bodyfat Data (part 1)

Lecture 8: Recursive Partitioning Demo with Bodyfat Data (part 2)

Lecture 9: Recursive Partitioning Demo with Bodyfat Data (part 3)

Lecture 10: Recursive Partitioning Demo with Glaucoma Data (part 1)

Lecture 11: Recursive Partitioning Demo with Glaucoma Data (part 2)

Lecture 12: Recursive Partitioning Demo with Glaucoma Data (part 3)

Chapter 8: Random Forests

Lecture 1: Recursive Partitioning Exercise Solutions

Lecture 2: Introduction to Random Forests

Lecture 3: Random Forest Rattle Tutorial with Weather Data (part 1)

Lecture 4: Random Forest Rattle Tutorial with Weather Data (part 2)

Lecture 5: Random Forest Rattle Tutorial with Weather Data (part 3)

Lecture 6: Random Forest Modeling with R Weather Data (part 1)

Lecture 7: Random Forest Modeling with R Weather Data (part 2)

Lecture 8: Random Forest Modeling with R Weather Data (part 3)

Lecture 9: Decision Tree Iris Data

Lecture 10: Random Forest Iris Data (part 1)

Lecture 11: Random Forest Iris Data (part 2)

Lecture 12: Random Forest Exercise

Chapter 9: Boosting

Lecture 1: Random Forest Exercise Solution (part 1)

Lecture 2: Random Forest Exercise Solution (part 2)

Lecture 3: Introduction to Boosting

Lecture 4: Boosting Tutorial using Rattle

Lecture 5: Basics of Boosting Demo using R

Lecture 6: Replicating Adaboost using Rpart (part 1)

Lecture 7: Replicating Adaboost using Rpart (part 2)

Lecture 8: Boosting Extensions and Variants

Lecture 9: Boosting Exercise

Chapter 10: Support Vector Machines

Lecture 1: Introduction to Support Vector Machines (SVMs)

Lecture 2: Boosting Exercise Solution

Lecture 3: Demonstrate Basis of SVM using R Scripts

Lecture 4: SVM Tutorial in Rattle

Lecture 5: SVM Model Evaluation (part 1)

Lecture 6: SVM Model Evaluation (part2)

Lecture 7: SVM Model Evaluation (part 3)

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 8 votes
2 stars: 6 votes
3 stars: 13 votes
4 stars: 33 votes
5 stars: 31 votes

Frequently Asked Questions

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