| Description |
1 online resource (xvi, 219 pages) : illustrations (some color) |
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text file |
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PDF |
| Note |
Includes index. |
| Contents |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Excel and Data Mining -- Why Excel? -- Prepare Some Excel Skills -- Formula -- Autofill or Copy -- Absolute Reference -- Paste Special and Paste Values -- IF Function Series -- Review Points -- Chapter 2: Linear Regression -- General Understanding -- Learn Linear Regression Through Excel -- Learn Multiple Linear Regression Through Excel -- Review Points -- Chapter 3: K-Means Clustering -- General Understanding -- Learn K-Means Clustering Through Excel |
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Review Points -- Chapter 4: Linear Discriminant Analysis -- General Understanding -- Solver -- Learn LDA Through Excel -- Review Points -- Chapter 5: Cross-Validation and ROC -- General Understanding of Cross-Validation -- Learn Cross-Validation Through Excel -- General Understanding of ROC Analysis -- Learn ROC Analysis Through Excel -- Review Points -- Chapter 6: Logistic Regression -- General Understanding -- Learn Logistic Regression Through Excel -- Review Points -- Chapter 7: K-Nearest Neighbors -- General Understanding -- Learn K-NN Through Excel -- Experiment 1 -- Experiment 2 |
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Experiment 3 -- Experiment 4 -- Review Points -- Chapter 8: Naïve Bayes Classification -- General Understanding -- Learn Naïve Bayes Through Excel -- Exercise 1 -- Exercise 2 -- Review Points -- Chapter 9: Decision Trees -- General Understanding -- Learn Decision Trees Through Excel -- Learn Decision Trees Through Excel -- A Better Approach -- Apply the Model -- Review Points -- Chapter 10: Association Analysis -- General Understanding -- Learn Association Analysis Through Excel -- Review Points -- Chapter 11: Artificial Neural Network -- General Understanding |
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Learn Neural Network Through Excel -- Experiment 1 -- Experiment 2 -- Review Points -- Chapter 12: Text Mining -- General Understanding -- Learn Text Mining Through Excel -- Review Points -- Chapter 13: After Excel -- Index |
| Summary |
Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. 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. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are 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 hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn: Comprehend data mining using a visual step-by-step approach Build on a theoretical introduction of a data mining method, followed by an Excel implementation Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone Become skilled in creative uses of Excel formulas and functions Obtain hands-on experience with data mining and Excel 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. Hong Zhou, PhD is a professor of computer science and mathematics and has been teaching c ourses in computer science, data science, mathematics, and informatics at the University of Saint Joseph for more than 15 years. His research interests include bioinformatics, data mining, software agents, and blockchain. Prior to his current position, he was as a Java developer in Silicon Valley. Dr. Zhou believes that learners can develop a better foundation of data mining models when they visually experience them step-by-step, which is what Excel offers. He has employed Excel in teaching data mining and finds it an effective approach for both data mining learners and educators. |
| Subject |
Microsoft Excel (Computer file)
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Microsoft Excel (Computer file) |
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Data mining.
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Machine learning.
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Data Mining |
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Machine Learning |
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Exploration de données (Informatique) |
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Apprentissage automatique. |
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Data mining. |
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Microsoft programming. |
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Data mining |
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Machine learning |
| Other Form: |
Print version: Zhou, Hong. Learn Data Mining Through Excel : A Step-By-Step Approach for Understanding Machine Learning Methods. Berkeley, CA : Apress L.P., ©2020 9781484259818 |
| ISBN |
9781484259825 electronic book |
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1484259823 electronic book |
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print |
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print |
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print |
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print |
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print |
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print |
| Standard No. |
10.1007/978-1-4842-5982-5. doi |
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10.1007/978-1-4842-5 |
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