Learn Data Mining Through Excel

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

eBook Details:

  • Paperback: 236 pages
  • Publisher: WOW! eBook; 1st edition (June 14, 2020)
  • Language: English
  • ISBN-10: 1484259815
  • ISBN-13: 978-1484259818

eBook Description:

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.

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.

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 Learn Data Mining Through Excel 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.

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