Harnessing the power of analytics / Leila Halawi, Amal Clarke, Kelly George.
Halawi, Leila, author.; Clarke, Amal, author.; George, Kelly, author.
2022
QA76.9.Q36 H35 2022
Available Online

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Details

Title
Harnessing the power of analytics / Leila Halawi, Amal Clarke, Kelly George.
Author
Halawi, Leila, author.
ISBN
9783030897123 (electronic bk.)
3030897125 (electronic bk.)
9783030897116
3030897117
DOI
https://doi.org/10.1007/978-3-030-89712-3
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (chiefly color).
Item Number
10.1007/978-3-030-89712-3 doi
Call Number
QA76.9.Q36 H35 2022
Dewey Decimal Classification
001.4/2
Summary
This text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 18, 2022).
Added Author
Clarke, Amal, author.
George, Kelly, author.
Available in Other Form
Print version: 9783030897116
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Online Access
Record Appears in
Online Resources > Ebooks
All Resources
Chapter 1. Introduction to Analytics and Data Science
Chapter 2. Data Types Structure & Data Preparation Process
Chapter 3. Data Exploration and Data Visualization. Chapter 4. Evaluating Predictive Performance
Chapter 5. Decision Trees & Ensemble
Chapter 6. Regression Models
Chapter 7. Neural Networks
Chapter 8. Model Deployment.

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