Engineering Analytics
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Engineering Analytics
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Author : Luis Rabelo
language : en
Publisher: CRC Press
Release Date : 2021-09-27
Engineering Analytics written by Luis Rabelo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-27 with Technology & Engineering categories.
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.
Enterprise Big Data Engineering Analytics And Management
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Author : Atzmueller, Martin
language : en
Publisher: IGI Global
Release Date : 2016-06-01
Enterprise Big Data Engineering Analytics And Management written by Atzmueller, Martin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-01 with Computers categories.
The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field.
Fundamentals Of Analytics Engineering
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Author : Dumky De Wilde
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-03-29
Fundamentals Of Analytics Engineering written by Dumky De Wilde and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-29 with Computers categories.
Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering Key Features Discover how analytics engineering aligns with your organization's data strategy Access insights shared by a team of seven industry experts Tackle common analytics engineering problems faced by modern businesses Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionNavigate the world of data analytics with Fundamentals of Analytics Engineering—guiding you from foundational concepts to advanced techniques of data ingestion and warehousing, data lakehouse, and data modeling. Written by a team of 7 industry experts, this book helps you to transform raw data into structured insights. You’ll discover how to clean, filter, aggregate, and reformat data, and seamlessly serve it across diverse platforms. With practical guidance, you’ll also learn how to build a simple data platform using Airbyte for ingestion, Google BigQuery for warehousing, dbt for transformations, and Tableau for visualization. From data quality and observability to fostering collaboration on codebases, you’ll find effective strategies for ensuring data integrity and driving collaborative success. As you advance, you'll become well-versed with the CI/CD principles for automated code building, testing, and deployment—laying the foundation for consistent and reliable pipelines. With invaluable insights into gathering business requirements, documenting complex business logic, and the importance of data governance, you’ll develop a holistic understanding of the analytics lifecycle. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Gain insights into the use of cloud-based analytics platforms and tools for scalable data processing Understand the principles of data governance and collaborative coding Comprehend data quality management in analytics engineering Gain practical skills in using analytics engineering tools to conquer real-world data challenges Who this book is for This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.
Advanced Analytics In Mining Engineering
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Author : Ali Soofastaei
language : en
Publisher: Springer Nature
Release Date : 2022-02-23
Advanced Analytics In Mining Engineering written by Ali Soofastaei and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-23 with Business & Economics categories.
In this book, Dr. Soofastaei and his colleagues reveal how all mining managers can effectively deploy advanced analytics in their day-to-day operations- one business decision at a time. Most mining companies have a massive amount of data at their disposal. However, they cannot use the stored data in any meaningful way. The powerful new business tool-advanced analytics enables many mining companies to aggressively leverage their data in key business decisions and processes with impressive results. From statistical analysis to machine learning and artificial intelligence, the authors show how many analytical tools can improve decisions about everything in the mine value chain, from exploration to marketing. Combining the science of advanced analytics with the mining industrial business solutions, introduce the “Advanced Analytics in Mining Engineering Book” as a practical road map and tools for unleashing the potential buried in your company’s data. The book is aimed at providing mining executives, managers, and research and development teams with an understanding of the business value and applicability of different analytic approaches and helping data analytics leads by giving them a business framework in which to assess the value, cost, and risk of potential analytical solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate IT and mining engineering students – with an understanding of data analytics applied to the mining industry. By providing a book with chapters structured in line with the mining value chain, we will provide a clear, enterprise-level view of where and how advanced data analytics can best be applied. This book highlights the potential to interconnect activities in the mining enterprise better. Furthermore, the book explores the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of creating a digital mine with much-enhanced capabilities for modeling, simulation, and the use of digital twins – in line with leading “digital” industries.
Data Analytics For Engineering And Construction Project Risk Management
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Author : Ivan Damnjanovic
language : en
Publisher: Springer
Release Date : 2019-05-23
Data Analytics For Engineering And Construction Project Risk Management written by Ivan Damnjanovic and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-23 with Technology & Engineering categories.
This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.
Strategic Engineering For Cloud Computing And Big Data Analytics
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Author : Amin Hosseinian-Far
language : en
Publisher: Springer
Release Date : 2017-02-13
Strategic Engineering For Cloud Computing And Big Data Analytics written by Amin Hosseinian-Far and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-13 with Technology & Engineering categories.
This book demonstrates the use of a wide range of strategic engineering concepts, theories and applied case studies to improve the safety, security and sustainability of complex and large-scale engineering and computer systems. It first details the concepts of system design, life cycle, impact assessment and security to show how these ideas can be brought to bear on the modeling, analysis and design of information systems with a focused view on cloud-computing systems and big data analytics. This informative book is a valuable resource for graduate students, researchers and industry-based practitioners working in engineering, information and business systems as well as strategy.
Principles Of Marketing Engineering 2nd Edition
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Author : Gary L. Lilien
language : en
Publisher: DecisionPro
Release Date : 2013
Principles Of Marketing Engineering 2nd Edition written by Gary L. Lilien and has been published by DecisionPro this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Business & Economics categories.
The 21st century business environment demands more analysis and rigor in marketing decision making. Increasingly, marketing decision making resembles design engineering-putting together concepts, data, analyses, and simulations to learn about the marketplace and to design effective marketing plans. While many view traditional marketing as art and some view it as science, the new marketing increasingly looks like engineering (that is, combining art and science to solve specific problems). Marketing Engineering is the systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process. (For more information on Excel-based models that support these concepts, visit DecisionPro.biz.) We have designed this book primarily for the business school student or marketing manager, who, with minimal background and technical training, must understand and employ the basic tools and models associated with Marketing Engineering. We offer an accessible overview of the most widely used marketing engineering concepts and tools and show how they drive the collection of the right data and information to perform the right analyses to make better marketing plans, better product designs, and better marketing decisions. What's New In the 2nd Edition While much has changed in the nearly five years since the first edition of Principles of Marketing Engineering was published, much has remained the same. Hence, we have not changed the basic structure or contents of the book. We have, however Updated the examples and references. Added new content on customer lifetime value and customer valuation methods. Added several new pricing models. Added new material on "reverse perceptual mapping" to describe some exciting enhancements to our Marketing Engineering for Excel software. Provided some new perspectives on the future of Marketing Engineering. Provided better alignment between the content of the text and both the software and cases available with Marketing Engineering for Excel 2.0.
Data Analytics Computational Statistics And Operations Research For Engineers
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Author : Debabrata Samanta
language : en
Publisher: CRC Press
Release Date : 2022-04-05
Data Analytics Computational Statistics And Operations Research For Engineers written by Debabrata Samanta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-05 with Technology & Engineering categories.
With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.
Probability Random Variables And Data Analytics With Engineering Applications
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Author : P. Mohana Shankar
language : en
Publisher: Springer Nature
Release Date : 2021-02-08
Probability Random Variables And Data Analytics With Engineering Applications written by P. Mohana Shankar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-08 with Technology & Engineering categories.
This book bridges the gap between theory and applications that currently exist in undergraduate engineering probability textbooks. It offers examples and exercises using data (sets) in addition to traditional analytical and conceptual ones. Conceptual topics such as one and two random variables, transformations, etc. are presented with a focus on applications. Data analytics related portions of the book offer detailed coverage of receiver operating characteristics curves, parametric and nonparametric hypothesis testing, bootstrapping, performance analysis of machine vision and clinical diagnostic systems, and so on. With Excel spreadsheets of data provided, the book offers a balanced mix of traditional topics and data analytics expanding the scope, diversity, and applications of engineering probability. This makes the contents of the book relevant to current and future applications students are likely to encounter in their endeavors after completion of their studies. A full suite of classroom material is included. A solutions manual is available for instructors. Bridges the gap between conceptual topics and data analytics through appropriate examples and exercises; Features 100's of exercises comprising of traditional analytical ones and others based on data sets relevant to machine vision, machine learning and medical diagnostics; Intersperses analytical approaches with computational ones, providing two-level verifications of a majority of examples and exercises.
Data Analytics For Drilling Engineering
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Author : Qilong Xue
language : en
Publisher: Springer Nature
Release Date : 2019-12-30
Data Analytics For Drilling Engineering written by Qilong Xue and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-30 with Science categories.
This book presents the signal processing and data mining challenges encountered in drilling engineering, and describes the methods used to overcome them. In drilling engineering, many signal processing technologies are required to solve practical problems, such as downhole information transmission, spatial attitude of drillstring, drillstring dynamics, seismic activity while drilling, among others. This title attempts to bridge the gap between the signal processing and data mining and oil and gas drilling engineering communities. There is an urgent need to summarize signal processing and data mining issues in drilling engineering so that practitioners in these fields can understand each other in order to enhance oil and gas drilling functions. In summary, this book shows the importance of signal processing and data mining to researchers and professional drilling engineers and open up a new area of application for signal processing and data mining scientists.