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Matrix Methods of Structural Analysis, 2nd Edition deals with the use of matrix methods as standard tools for solving most non-trivial problems of structural analysis. Emphasis is on skeletal structures and the use of a more general finite element approach. The methods covered have natural links with techniques for automatic redundant selection in elastic analysis. This book is comprised of 11 chapters and begins with an introduction to the concepts and notation of matrix algebra, along with the value of a systematic approach; structure as an assembly of elements; boundaries and nodes; linearity and superposition; and how analytical methods are built up. The discussion then turns to the variables which form the basis of much of structural analysis, as well as the most important relationships between them. Subsequent chapters focus on the elastic properties of single elements; the equilibrium or displacement method; the equilibrium equations of a complete structure; plastic analysis and design; transfer matrices; and the analysis of non-linear structures. The compatibility or force method is also described. The final chapter considers the limits imposed by the size and accuracy of the computer used in structural analysis and how they can be extended. This monograph will be of interest to structural engineers and students of engineering.

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track

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SGN. The KRCL Exam PDF-Konkan Railway Corporation Ltd-Senior Section Engineer (Civil) Exam-Civil Engineering Subject Practice Sets eBook Covers Objective Questions With Answers.