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Statistics for Data Science

You're reading from   Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Length 286 pages
Edition 1st Edition
Languages
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Table of Contents (13) Chapters Close

Preface 1. Transitioning from Data Developer to Data Scientist 2. Declaring the Objectives FREE CHAPTER 3. A Developer's Approach to Data Cleaning 4. Data Mining and the Database Developer 5. Statistical Analysis for the Database Developer 6. Database Progression to Database Regression 7. Regularization for Database Improvement 8. Database Development and Assessment 9. Databases and Neural Networks 10. Boosting your Database 11. Database Classification using Support Vector Machines 12. Database Structures and Machine Learning

Understanding basic data cleaning


The importance of having clean (and therefore reliable) data in any statistical project cannot be overstated. Dirty data, even with sound statistical practice, can be unreliable and can lead to producing results that suggest courses of action that are incorrect or that may even cause harm or financial loss. It has been stated that a data scientist spends nearly 90 percent of his or her time in the process of cleaning data and only 10 percent on the actual modeling of the data and deriving results from it.

So, just what is data cleaning?

Data cleaning is also referred to as data cleansing or data scrubbing and involves both the processes of detecting as well as addressing errors, omissions, and inconsistencies within a population of data.

This may be done interactively with data wrangling tools, or in batch mode through scripting. We will use R in this book as it is well-fitted for data science since it works with even very complex datasets, allows handling...

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