Good data analytics is the basis for effective decisions. Whoever has the data, has the ability to extract information promptly and effectively to make pertinent decisions. The premise of this handbook is to empower users and tool developers with the appropriate collection of formulas and techniques for data analytics and to serve as a quick reference to keep pertinent formulas within fingertip reach of readers.
This handbook includes formulas that will appeal to mathematically inclined readers. It discusses how to use data analytics to improve decision-making and is ideal for those new to using data analytics to show how to expand their usage horizon. It provides quantitative techniques for modeling pandemics, such as COVID-19. It also adds to the suite of mathematical tools for emerging technical areas.
This handbook is a handy reference for researchers, practitioners, educators, and students in areas such as industrial engineering, production engineering, project management, civil engineering, mechanical engineering, technology management, and business management worldwide.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Academic, Postgraduate, Professional, and Undergraduate Advanced
Illustrationen
1 s/w Photographie bzw. Rasterbild, 32 s/w Zeichnungen, 20 s/w Tabellen, 33 s/w Abbildungen
20 Tables, black and white; 32 Line drawings, black and white; 1 Halftones, black and white; 33 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
Dicke: 15 mm
Gewicht
ISBN-13
978-0-367-53742-5 (9780367537425)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Adedeji B. Badiru is a professor of systems engineering at the Graduate School of Engineering and Management at the Air Force Institute of Technology. He was previously Professor and Department Head of Industrial Engineering at the University of Tennessee, and Professor of Industrial Engineering at the University of Oklahoma. He is a registered professional engineer (PE), a certified Project Management Professional (PMP), and a Fellow of the Institute of Industrial & Systems Engineers. He is the author of over 30 books, 34 book chapters, and 80 technical journal articles. He is a member of several professional associations and scholastic honor societies.
Autor*in
Air Force Institute of Technology, Dayton, Ohio, USA
Chapter 1. Essential Data Analytics. Chapter 2. Empirical Model Building. Chapter 3. Data Visualization Methods. Chapter 4. Basic Mathematical Calculations for Data Analytics. Chapter 5. Statistical Methods for Data Analytics. Chapter 6. Descriptive Statistics for Data Presentation. Chapter 7. Data Analytics Tools for Understanding Random Field Regression Models. Chapter 8. Application of DEJI Systems Model to Data Integration.
