Video Description
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 tool
- Gain a practical hands-on understanding of the core methods used in data analytics
- Explore correlational methods, affinity analysis methods, and predictive methods
- Discover which analytical method works best for a specific type of data
- Learn how to apply a selected method to build a model in RapidMiner and interpret its results
Table of Contents
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Introduction
- Welcome To The Course 00:01:59
- About The Author 00:01:15
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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
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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
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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
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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
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Unstructured Data
- Text Analysis 00:07:59
- Now You Try 00:03:32
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Evaluation And Deployment
- Cross-Validation 00:04:55
- A Note About Ethics 00:03:10
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Conclusion
- Wrap Up And Thank You 00:03:14