Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Download or read book Concise Metals Engineering Data Book written by Joseph R. Davis and published by ASM International. This book was released on 1997-01-01 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Perspectives on Data Science for Software Engineering written by Tim Menzies and published by Morgan Kaufmann. This book was released on 2016-07-14 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community's leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. - Presents the wisdom of community experts, derived from a summit on software analytics - Provides contributed chapters that share discrete ideas and technique from the trenches - Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data - Presented in clear chapters designed to be applicable across many domains

Download or read book ASME Engineer s Data Book written by Clifford Matthews and published by American Society of Mechanical Engineers. This book was released on 2005 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This greatly expanded second edition of this popular and handy reference book includes over 100 new pages, including extensive coverage of Section VIII of the ASME Pressure Vessel Code. Divided into 22 sections, this pocket-sized volume is an exhaustive "quick reference" of up-to-date engineering data and rules. It includes: essential mathematics; units; engineering design processes and principles; basic mechanical design; motion; mechanics of materials; material failure; thermodynamics; fluid mechanics; fluid equipment; vessel codes and standards; materials; machine elements; design and production tools; project engineering; computer-aided engineering; welding; non-destructive examination; corrosion; surface protection; metallurgical terms; and engineering associations and organizations.

Download or read book Data Driven Engineering Design written by Ang Liu and published by Springer Nature. This book was released on 2021-10-09 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design. Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation. Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design.

Download or read book Advances in Engineering Data Handling written by P.C.C. Wang and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: To understand what we know and be aware of what is to be known has become the central focus in the treatment of engineering data handling issues. It has been some time since we began treating issues arriving from engineering data handling in a low key fashion because of its housekeeping chores and data maintenance aspects representing nonglamorous issues related to automation. Since the advent of CAD/CAM, large numbers of data bases have been generated through stand alone CAD systems and the rate of this automated means of generating data is rapidly increasing. This possibly is the key factor in changing our way of looking at engineering data related problems. This volume contains some of the papers, including revisions, which were presented at the fourth Automation Technology conference held in Monterey, California. This volume represents ATI's efforts to bring forth some of the important case studies related to engineering data handling from the user's point of view. Because of its potential enormous impact on management and productivity advancement, careful documentation and coordination for outstanding contributions to this area are of utmost importance. This volume may serve as a precursor to additional volumes in the area of engineering data handling and CAD/CAM related user studies. Anyone with comments or suggestions, as well as potential contributors, to this series, is encouraged to contact the editorial board of AT!.

Download or read book Data Analytics for Engineering and Construction Project Risk Management written by Ivan Damnjanovic and published by Springer. This book was released on 2019-05-23 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book 97 Things Every Data Engineer Should Know written by Tobias Macey and published by "O'Reilly Media, Inc.". This book was released on 2021-06-11 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail

Download or read book Data Engineering on Azure written by Vlad Riscutia and published by Simon and Schuster. This book was released on 2021-08-17 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data

Download or read book Statistics and Data Analysis for Financial Engineering written by David Ruppert and published by Springer. This book was released on 2015-04-21 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.

Download or read book Data Engineering written by Olaf Wolkenhauer and published by John Wiley & Sons. This book was released on 2004-04-07 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although data engineering is a multi-disciplinary field withapplications in control, decision theory, and the emerging hot areaof bioinformatics, there are no books on the market that make thesubject accessible to non-experts. This book fills the gap in thefield, offering a clear, user-friendly introduction to the maintheoretical and practical tools for analyzing complex systems. Anftp site features the corresponding MATLAB and Mathematical toolsand simulations. Market: Researchers in data management, electrical engineering,computer science, and life sciences.

Download or read book Fundamentals of Data Engineering written by Joe Reis and published by "O'Reilly Media, Inc.". This book was released on 2022-06-22 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle

Download or read book Data Pipelines Pocket Reference written by James Densmore and published by O'Reilly Media. This book was released on 2021-02-10 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting

Download or read book Data Reverse Engineering written by Peter H. Aiken and published by McGraw-Hill Companies. This book was released on 1996 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Privacy written by Nishant Bhajaria and published by Simon and Schuster. This book was released on 2022-03-22 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: Engineer privacy into your systems with these hands-on techniques for data governance, legal compliance, and surviving security audits. In Data Privacy you will learn how to: Classify data based on privacy risk Build technical tools to catalog and discover data in your systems Share data with technical privacy controls to measure reidentification risk Implement technical privacy architectures to delete data Set up technical capabilities for data export to meet legal requirements like Data Subject Asset Requests (DSAR) Establish a technical privacy review process to help accelerate the legal Privacy Impact Assessment (PIA) Design a Consent Management Platform (CMP) to capture user consent Implement security tooling to help optimize privacy Build a holistic program that will get support and funding from the C-Level and board Data Privacy teaches you to design, develop, and measure the effectiveness of privacy programs. You’ll learn from author Nishant Bhajaria, an industry-renowned expert who has overseen privacy at Google, Netflix, and Uber. The terminology and legal requirements of privacy are all explained in clear, jargon-free language. The book’s constant awareness of business requirements will help you balance trade-offs, and ensure your user’s privacy can be improved without spiraling time and resource costs. About the technology Data privacy is essential for any business. Data breaches, vague policies, and poor communication all erode a user’s trust in your applications. You may also face substantial legal consequences for failing to protect user data. Fortunately, there are clear practices and guidelines to keep your data secure and your users happy. About the book Data Privacy: A runbook for engineers teaches you how to navigate the trade-off s between strict data security and real world business needs. In this practical book, you’ll learn how to design and implement privacy programs that are easy to scale and automate. There’s no bureaucratic process—just workable solutions and smart repurposing of existing security tools to help set and achieve your privacy goals. What's inside Classify data based on privacy risk Set up capabilities for data export that meet legal requirements Establish a review process to accelerate privacy impact assessment Design a consent management platform to capture user consent About the reader For engineers and business leaders looking to deliver better privacy. About the author Nishant Bhajaria leads the Technical Privacy and Strategy teams for Uber. His previous roles include head of privacy engineering at Netflix, and data security and privacy at Google. Table of Contents PART 1 PRIVACY, DATA, AND YOUR BUSINESS 1 Privacy engineering: Why it’s needed, how to scale it 2 Understanding data and privacy PART 2 A PROACTIVE PRIVACY PROGRAM: DATA GOVERNANCE 3 Data classification 4 Data inventory 5 Data sharing PART 3 BUILDING TOOLS AND PROCESSES 6 The technical privacy review 7 Data deletion 8 Exporting user data: Data Subject Access Requests PART 4 SECURITY, SCALING, AND STAFFING 9 Building a consent management platform 10 Closing security vulnerabilities 11 Scaling, hiring, and considering regulations

Download or read book Aeronautical Engineer s Data Book written by Cliff Matthews and published by Elsevier. This book was released on 2001-10-17 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aeronautical Engineer's Data Bookis an essential handy guide containing useful up to date information regularly needed by the student or practising engineer. Covering all aspects of aircraft, both fixed wing and rotary craft, this pocket book provides quick access to useful aeronautical engineering data and sources of information for further in-depth information. - Quick reference to essential data - Most up to date information available

Download or read book Exploring Data in Engineering the Sciences and Medicine written by Ronald Pearson and published by Oxford University Press. This book was released on 2011-02-03 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces various widely available exploratory data analysis methods, emphasizing those that are most useful in the preliminary exploration of large datasets involving mixed data types. Topics include descriptive statistics, graphical analysis tools, regression modeling and spectrum estimation, along with practical issues like outliers, missing data, and variable selection.