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Data Mining for the Masses—5th ed.

Data Analytics

ISBN #978-1-959142-65-2

Data Mining for the Masses—5th ed.

Matt North, EdD

This resource introduces students to data mining using the CRISP-DM methodology. It is intended for students and business professionals who are interested in using information systems and technologies to solve organizational problems by mining data, but who may not have a background in computer science. Students will learn the concepts and techniques required to successfully mine data in AI Studio, R, and Python.

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Course Objective

This resource teaches students and business professionals without a computer science background how to use the CRISP-DM methodology to mine data and solve organizational problems using AI Studio, R, and Python.

Table of Contents

About the Author
Chapter 1: Introduction to Data Mining and CRISP-DM
Chapter 2: Organizational Understanding and Data Understanding
Chapter 3: Data Preparation
Chapter 4: Correlational Methods
Chapter 5: Association Rules
Chapter 6: k-Means Clustering
Chapter 7: Predicting Categories
Chapter 8: Linear Regression
Chapter 9: Logistic Regression
Chapter 10: Decision Trees
Chapter 11: Neural Networks
Chapter 12: Text Mining
Chapter 13: Cross-Validation
Chapter 14: Data Mining Ethics

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