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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
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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

Data analysis


Let's start by looking at what is known as data analysis. This is defined as a structured process undertaken to evaluate data using analytical and logical reasoning. One performs data analysis by taking the time to gather up all the data to be analyzed, breaking that data (now viewed as a data source) into chunks or components (that can be reviewed), and then drawing a conclusion based upon what is seen or found within the data. Typically, this is done in an effort to determine that a data source is useable for meeting a declared project deliverable.

There are a variety of specific data analysis approaches, some of which include data mining (discussed in Chapter 4, Data Mining and the Database Developer), text analytics, business intelligence, and data visualizations (just to name a few of them).

To a data developer, data analysis involves inspecting the individual parts of a data source with an intention in mind.

For example, suppose we have some transactional data collected...

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