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Statistics for Data Science

You're reading from  Statistics for Data Science

Product type Book
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Pages 286 pages
Edition 1st Edition
Languages
Concepts

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Transitioning from Data Developer to Data Scientist 2. Declaring the Objectives 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

Assessment and statistical assessment


Merriam-Webster definesassessment as:

The action or an instance of making a judgment about something.

The following image shows flow for assessing statistical data:

We need to keep a few pointers in mind for statistical assessment. They are listed as follows.

Objectives

With that in mind, to be able to make a reasonable assessment--that is, make a judgment--on something (anything really), one must first have to set objective(s). Assessment objectives help the data scientist determine how to assess data, a database, or a statistical data model. Without clear objectives, you'll waste valuable time, and potentially, put confidence in a model that doesn't meet a business requirement or may even lead to incorrect assumptions (predictions).

Baselines

Next, (based on your set objectives) standards, minimum acceptable performance, or a baseline to establish an opinion on what is being assessed need to be established. In other words, how well does what you are assessing...

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