by Matthew North | Released September 2016 | ISBN: 9781491969120

This course is designed for the person who is new to the science of data analytics, who has completed at least one college-level math class, and is comfortable with basic statistics. The course explains the core methods used in data analytics and how to apply those methods in conjunction with RapidMiner, a free and easy-to-use (no programming knowledge required) data analytics platform.You'll first learn about the features of RapidMiner, configuring it, and how to connect to a variety of data sets, and then move into a detailed survey of the analytical methods incorporated within the software. Topics covered include correlation, association rules, k-means clustering, k-nearest neighbors, discriminant analysis, Naive Bayes, linear and logistic regression, neural networks, decision trees, and text analysis.Learn how to use RapidMiner as a data analytics toolGain a practical hands-on understanding of the core methods used in data analyticsExplore correlational methods, affinity analysis methods, and predictive methodsDiscover which analytical method works best for a specific type of dataLearn how to apply a selected method to build a model in RapidMiner and interpret its resultsProfessor Matt North teaches data analytics and data mining at Utah Valley University. He is a Fulbright alumnus, a recipient of a Gamma Sigma Alpha Outstanding Professor Award, and the author of the book "Data Mining for the Masses". He holds a Doctor of Education degree from West Virginia University and a Master of Science from Utah State University. Show and hide more Publisher resources Download Example Code

1. Introduction
   • Welcome To The Course 00:01:59
   • About The Author 00:01:15
2. Preparation
   • Setting Up The RapidMiner Environment 00:05:00
   • RM 7.2 Downsampling Explanation 00:01:17
   • Data Sets 00:09:22
   • Now You Try 00:01:55
3. Modeling Techniques: Correlational Methods
   • Correlation 00:08:21
   • K-Means Clustering 00:07:44
   • K-Nearest Neighbors 00:06:55
   • Now You Try 00:03:21
4. Modeling Techniques: Affinity Analysis
   • Association Rules 00:05:00
   • Naive Bayes 00:04:49
   • Discriminant Analysis 00:02:52
   • Now You Try 00:05:13
5. Modeling Techniques: Prediction
   • Linear Regression 00:03:50
   • Logistic Regression 00:05:46
   • Decision Trees 00:04:35
   • Neural Networks 00:05:18
   • Now You Try 00:07:41
6. Unstructured Data
   • Text Analysis 00:07:59
   • Now You Try 00:03:32
7. Evaluation And Deployment
   • Cross-Validation 00:04:55
   • A Note About Ethics 00:03:10
8. Conclusion
   • Wrap Up And Thank You 00:03:14
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