Название: Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods, 2nd EditionАвтор: Hong Zhou
Издательство: Apress
Год: 2023
Страниц: 289
Язык: английский
Формат: pdf (true), epub (true)
Размер: 31.9 MB
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.
If you are already an experienced data mining professional, I would say that you are asking the right question and probably you should not read this book. However, if you are a beginner in data mining, or a visual learner, or want to understand the mathematical background behind some popular data mining techniques, or an educator, then this book is right for you, and probably is the first book you should read before you start your data mining journey.
Excel allows you to work with data in a transparent manner, meaning when an Excel file is opened, the data is visible immediately and every step of data processing is also visible. Intermediate results are contained in the Excel worksheet and can be examined while you are conducting your mining task. This allows you to obtain a deep and clear understanding of how the data are manipulated and how the results are obtained. Other software tools and programming languages hide critical aspects of the model construction process. For most data mining projects, the goal is to find the internal hidden patterns inside the data. Therefore, hiding the detailed process is beneficial to the users of the tools or packages. But it is not helpful for beginners, visual learners, or those who want to understand how the mining process works. Let me use k-nearest neighbors method (K-NN) to illustrate the learning differences between RapidMiner, R, and Excel.
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.
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