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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

Identifying opportunities for statistical regression


Typical statistical analysis efforts which often become official statistical projects, start out with determining an objective and then, ultimately, determining the right approach to meet that objective.

Popular data science opinion declares determining an objective as establishing the purpose of a statistical analysis effort, then splits the purpose into three areas:

  1. Summarizing data (also called building a data profile)
  2. Exposing and exploring relationships between variables in the data
  3. Testing the significance of differences (between variables or groups within the data)

Summarizing data

If your statistical objective is to summarize data, you generate descriptive statistics, such as mean, standard deviation, variances, and so on.

Exploring relationships

If your statistical objective is to look for and learn about relationships in your data, you first examine your data for a form or, in other words, ask the question: does your data revolve around...

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