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Introduction to R for Business Intelligence

You're reading from   Introduction to R for Business Intelligence Profit optimization using data mining, data analysis, and Business Intelligence

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Product type Paperback
Published in Aug 2016
Publisher Packt
ISBN-13 9781785280252
Length 228 pages
Edition 1st Edition
Languages
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Author (1):
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Jay Gendron Jay Gendron
Author Profile Icon Jay Gendron
Jay Gendron
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Table of Contents (13) Chapters Close

Preface 1. Extract, Transform, and Load FREE CHAPTER 2. Data Cleaning 3. Exploratory Data Analysis 4. Linear Regression for Business 5. Data Mining with Cluster Analysis 6. Time Series Analysis 7. Visualizing the Datas Story 8. Web Dashboards with Shiny A. References
B. Other Helpful R Functions C. R Packages Used in the Book
D. R Code for Supporting Market Segment Business Case Calculations

Finding and fixing flawed data

The summarize step revealed some possible flaws in the data. Mind you, the bike sharing data is in good shape compared to many datasets that you will encounter as a business analyst. Your next step in the SFCA approach is to fix any flaws that could impact your analysis. First, you have to find the flaws before you can fix them.

Finding flaws in datasets

There is no single best way to find flaws in data. This activity requires art mixed with some computational methods. The approach presented in this section shares some methods, but they only represent a few of the many possible ways of finding flawed data.

Tip

R tip: Keep an open mind and strive to become a lifelong learner of business analytics. The methods change and adapt continually. The best business analysts have a skillset that goes beyond great coding. Throughout this book, the authors will share their favorite picks from videos, blogs, and books relevant to the chapters.

One tip for business analysts...

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