Learn Data Mining Through Excel - A Step-by-Step Approach for Understanding Machine Learning Methods

Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Most software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help, and this book will show you exactly how.



This updated edition demonstrates how to work with data in a transparent manner using Excel. When you open an Excel file, data is visible immediately and you can work with it directly. You'll see how to examine intermediate results even as you are still conducting your mining task, offering a deeper understanding of how data is manipulated, and results are obtained. These are critical aspects of the model construction process that are often hidden in software tools and programming language packages.



Over the course of Learn Data Mining Through Excel, you will learn the data mining advantages the application offers when the data sets are not too large. You'll see how to use Excel's built-in features to create visual representations of your data, enabling you to present your findings in an accessible format. Author Hong Zhou walks you through each step, offering not only an active learning experience, but teaching you how the mining process works and how to find hidden patterns within the data.



Upon completing this book, you will have a thorough understanding of how to use an application you very likely already have to mine and analyze data, and how to present results in various formats.



What You Will Learn

Comprehend data mining using a visual step-by-step approach
Gain an introduction to the fundamentals of data mining
Implement data mining methods in Excel
Understand machine learning algorithms
Leverage Excel formulas and functions creatively
Obtain hands-on experience with data mining and Excel


Who This Book Is For

Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.

List of contents

Chapter 1: Excel and Data Mining.- Chapter 2: Linear Regression.- Chapter 3: K-Means Clustering.- Chapter 4: Linear Discriminant Analysis.- Chapter 5: Cross Validation and ROC.- Chapter 6: Logistic Regression.- Chapter 7: K-nearest Neighbors.- Chapter 8: Naïve Bayes Classification.- Chapter 9: Decision Trees.- Chapter 10: Association Analysis.- Chapter 11: Artificial Neural Networks.- Chapter 12: Text Mining.- Chapter 13: Hierarchical Clustering and Dendrogram.- Chapter 14 Exploratory Data Analysis (EDA).- Chapter 15: After Excel.

About the author

Hong Zhou is a professor of computer science and mathematics at the University of Saint Joseph in Connecticut. Before returning to school for his doctoral degree, Dr. Zhou worked as a Java developer in Silicon Valley. Since 2004, Dr. Zhou has been teaching various courses in computer science, data science, mathematics, statistics, and informatics. His major research interests include data mining, bioinformatics, software agents, and blockchain. Dr. Zhou became interested in Excel through teaching computer skills and using them for research purposes; for example, applying Excel in teaching data mining, encryption, and health informatics. He also enjoys applying his Excel skills to help colleagues in their research projects.

Product details

• Authors: Hong Zhou
• Publisher: Apress
• Languages: English
• Product format: Paperback / Softback
• Released: 04.11.2023
• EAN: 9781484297704
• ISBN: 978-1-4842-9770-4
• No. of pages: 300
• Dimensions: 178 mm x 254 mm x 17 mm
• Weight: 569 g
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT