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Harnessing the Power of Analytics
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Harnessing the Power of Analytics/ by Leila Halawi, Amal Clarke, Kelly George.
作者:
Halawi, Leila.
其他作者:
George, Kelly.
面頁冊數:
VIII, 150 p. 106 illus., 90 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
IT in Business. -
電子資源:
https://doi.org/10.1007/978-3-030-89712-3
ISBN:
9783030897123
Harnessing the Power of Analytics
Halawi, Leila.
Harnessing the Power of Analytics
[electronic resource] /by Leila Halawi, Amal Clarke, Kelly George. - 1st ed. 2022. - VIII, 150 p. 106 illus., 90 illus. in color.online resource.
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.
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.
ISBN: 9783030897123
Standard No.: 10.1007/978-3-030-89712-3doiSubjects--Topical Terms:
1064965
IT in Business.
LC Class. No.: TK5101-5105.9
Dewey Class. No.: 621.382
Harnessing the Power of Analytics
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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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