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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

Development versus assessment


Although an assessment process does produce output, which ultimately is perhaps just a decision (that is, does the data, database, or statistical data model under observation meet the acceptable limits of performance, based on the objectives?), development implies building.

Development can also mean improving by expanding, enlarging, or refining. This means that (or at least it implies that) whatever one is developing may never be completely done. In fact, development and assessment do go hand in hand.

An industry-proven practice recommendation to develop anything is as follows:

  • Build (or develop)
  • Test
  • Assess
  • Repeat

When developing a relational data model, one might utilize a create SQL statement, something like the following code:

mysql> CREATE TABLE test (a INT NOT NULL AUTO_INCREMENT,
-> PRIMARY KEY (a), KEY(b))
-> ENGINE=MyISAM SELECT b,c FROM test2;

Dissecting the preceding code, we can see that the outcome is that a table object test is generated. Perhaps...

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