Engineering Analytics Advances in Research and Applications
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Engineering Analytics Advances in Research and Applications
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Engineering Analytics Advances in Research and Applications

Author(s)/Editor(s): Luis Rabelo, Edgar Gutierrez-Franco, Alfonso Sarmiento, Christopher Mejía-Argueta | Size: 33 MB | Format: PDF | Quality: Original preprint | Publisher: CRC Press (Taylor & Francis Ltd) | Year: September 27, 2021 | pages: 268 | ISBN: 9780367685348


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Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers.

Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints.

The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data.

Table of Contents

Section 1: Introduction to Analytics.
1. Evolution of Data Analytics in the Last Decades.
2. Business Analytics Life-Cycle.
3. Main Organizational Paradigms Related to Analytics.

Section 2: Visual Analytics.
4. Logistics Observatories.
5. Commercial Systems - Tableau, Power BI.
6. Types of Graphs.
7. Definition of Key Performance Indicators.
8. Case Studies.

Section 3: Descriptive Analytics.
9. Probability Distributions.
10. Covariance and Correlation.
11. Linear and Non-Linear Regression.
12. Dimension Reduction.
13. Case Studies.

Section 4: Predictive and Prescriptive Analytics.
14. Neural Networks and Support Vector Machines.
15. Discrete, Dynamic and Agent-Based Simulation.
16. Mathematical Modeling, Deterministic and Stochastic Optimization.
17. Case Studies. Section 5: Strategic Implications of Analytics.
18. Strategic Maps.
19. Balanced Scorecard.
20. Scenario Analysis.

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