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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

Successful statistical analysis


It is worthwhile to mention some key points in this section, dealing with ensuring a successful (or at least productive) statistical analysis effort.

Note

You may find that most of these are perhaps common sense notions, but some may not be.

  1. As soon as you can, decide on your goal or objective. You need to know what the win is, that is, what the problem or idea is that is driving the analysis effort. In addition, you need to make sure that, whatever is driving the analysis, the result obtained must be measurable in some way. This metric or performance indicator must be identified early.
  2. Identify key levers. This means that once you have established your goals and a way to measure performance towards obtaining those goals, you also need to find out what has an effect on the performance towards obtaining each goal.
  3. Conduct a thorough data collection. Typically, the more data the better, but in the absence of quantity, always go with quality.
  4. Clean your data. Make...
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