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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
Transitioning from Data Developer to Data Scientist Declaring the Objectives A Developer's Approach to Data Cleaning Data Mining and the Database Developer Statistical Analysis for the Database Developer Database Progression to Database Regression Regularization for Database Improvement Database Development and Assessment Databases and Neural Networks Boosting your Database Database Classification using Support Vector Machines Database Structures and Machine Learning

Database classification


As we've said throughout this book, if the reader is a data or database developer, or has a similar background, the reader will most likely have heard of and be familiar with and comprehend the process of data modeling. This can be (at a high level, perhaps) described as the effort of analyzing and understanding the makeup and details of some data. Then, this data is organized or classified with the objective being that it can be easily understood and consumed by a community of users, either named (those already identified, such as financial analysts in an organization) or anonymous (such as internet consumers). The following image shows the data classification as per requirements:

Note

Anonymous access is the most common system access control method, at least, when it comes to websites.

As part of almost any data modeling development project, one might be asked to create a class diagram. This (class) diagram details how to split data (or a database) into discrete objects...

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