Search results for: data-architecture-a-primer-for-the-data-scientist

Data Architecture A Primer for the Data Scientist

Author : W.H. Inmon
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Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data

Hands On Big Data Modeling

Author : James Lee
File Size : 22.75 MB
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Solve all big data problems by learning how to create efficient data models Key Features Create effective models that get the most out of big data Apply your knowledge to datasets from Twitter and weather data to learn big data Tackle different data modeling challenges with expert techniques presented in this book Book Description Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently. What you will learn Get insights into big data and discover various data models Explore conceptual, logical, and big data models Understand how to model data containing different file types Run through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modeling Create data models such as Graph Data and Vector Space Model structured and unstructured data using Python and R Who this book is for This book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.

Relational Database Design and Implementation

Author : Jan L. Harrington
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Relational Database Design and Implementation: Clearly Explained, Fourth Edition, provides the conceptual and practical information necessary to develop a database design and management scheme that ensures data accuracy and user satisfaction while optimizing performance. Database systems underlie the large majority of business information systems. Most of those in use today are based on the relational data model, a way of representing data and data relationships using only two-dimensional tables. This book covers relational database theory as well as providing a solid introduction to SQL, the international standard for the relational database data manipulation language. The book begins by reviewing basic concepts of databases and database design, then turns to creating, populating, and retrieving data using SQL. Topics such as the relational data model, normalization, data entities, and Codd's Rules (and why they are important) are covered clearly and concisely. In addition, the book looks at the impact of big data on relational databases and the option of using NoSQL databases for that purpose. Features updated and expanded coverage of SQL and new material on big data, cloud computing, and object-relational databases Presents design approaches that ensure data accuracy and consistency and help boost performance Includes three case studies, each illustrating a different database design challenge Reviews the basic concepts of databases and database design, then turns to creating, populating, and retrieving data using SQL

Conceptual Modeling

Author : Alberto H. F. Laender
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This book constitutes the refereed proceedings of the 38th International Conference on Conceptual Modeling, ER 2019, held in Salvador, Brazil, in November 2019. The 22 full and 22 short papers presented together with 4 keynotes were carefully reviewed and selected from 142 submissions. This events covers a wide range of topics, covered in the following sessions: conceptual modeling, big data technology I, process modeling and analysis, query approaches, big data technology II, domain specific models I, domain specific models II, decision making, complex systems modeling, model unification, big data technology III, and requirements modeling.

Data Science for Transport

Author : Charles Fox
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The quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of "big data", "Data Science", and "smart cities" changing the world, with the Harvard Business Review describing Data Science as the "sexiest job of the 21st century". Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues. "Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds". - Tom van Vuren, Divisional Director, Mott MacDonald "WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book." - Leighton Cardwell, Technical Director, WSP. "From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to "talk" to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds." - Fitsum Teklu, Associate Director (Modelling & Appraisal) SYSTRA Ltd "Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant toanyone who will later want to build digital solutions to optimise urban travelbased on emerging data sources". - Yaron Hollander, author of "Transport Modelling for a Complete Beginner"

R Data Mining

Author : Andrea Cirillo
File Size : 48.62 MB
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Mine valuable insights from your data using popular tools and techniques in R About This Book Understand the basics of data mining and why R is a perfect tool for it. Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. Apply effective data mining models to perform regression and classification tasks. Who This Book Is For If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required. What You Will Learn Master relevant packages such as dplyr, ggplot2 and so on for data mining Learn how to effectively organize a data mining project through the CRISP-DM methodology Implement data cleaning and validation tasks to get your data ready for data mining activities Execute Exploratory Data Analysis both the numerical and the graphical way Develop simple and multiple regression models along with logistic regression Apply basic ensemble learning techniques to join together results from different data mining models Perform text mining analysis from unstructured pdf files and textual data Produce reports to effectively communicate objectives, methods, and insights of your analyses In Detail R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets. Style and approach This book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.

SQL Server 2017 Machine Learning Services with R

Author : Tomaz Kastrun
File Size : 72.23 MB
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Develop and run efficient R scripts and predictive models for SQL Server 2017 Key Features Learn how you can combine the power of R and SQL Server 2017 to build efficient, cost-effective data science solutions Leverage the capabilities of R Services to perform advanced analytics—from data exploration to predictive modeling A quick primer with practical examples to help you get up- and- running with SQL Server 2017 Machine Learning Services with R, as part of database solutions with continuous integration / continuous delivery. Book Description R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services. Delving more deeply into predictive modeling and the RevoScaleR package, this book also provides insights into operationalizing code and exploring and visualizing data. To complete the journey, this book covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power. What you will learn Get an overview of SQL Server 2017 Machine Learning Services with R Manage SQL Server Machine Learning Services from installation to configuration and maintenance Handle and operationalize R code Explore RevoScaleR R algorithms and create predictive models Deploy, manage, and monitor database solutions with R Extend R with SQL Server 2017 features Explore the power of R for database administrators Who this book is for This book is for data analysts, data scientists, and database administrators with some or no experience in R but who are eager to easily deliver practical data science solutions in their day-to-day work (or future projects) using SQL Server.

Applied Science Technology Index

Author :
File Size : 33.36 MB
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iRODS Primer 2

Author : Hao Xu
File Size : 34.38 MB
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Policy-based data management enables the creation of community-specific collections. Every collection is created for a purpose. The purpose defines the set of properties that will be associated with the collection. The properties are enforced by management policies that control the execution of procedures that are applied whenever data are ingested or accessed. The procedures generate state information that defines the outcome of enforcing the management policy. The state information can be queried to validate assessment criteria and verify that the required collection properties have been conserved. The integrated Rule-Oriented Data System implements the data management framework required to support policy-based data management. Policies are turned into computer actionable Rules. Procedures are composed from a microservice-oriented architecture. The result is a highly extensible and tunable system that can enforce management policies, automate administrative tasks, and periodically validate assessment criteria. iRODS 4.0+ represents a major effort to analyze, harden, and package iRODS for sustainability, modularization, security, and testability. This has led to a fairly significant refactorization of much of the underlying codebase. iRODS has been modularized whereby existing iRODS 3.x functionality has been replaced and provided by small, interoperable plugins. The core is designed to be as immutable as possible and serve as a bus for handling the internal logic of the business of iRODS. Seven major interfaces have been exposed by the core and allow extensibility and separation of functionality into plugins.

Datamation

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File Size : 54.40 MB
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1984 Complete Sourcebook of Personal Computing

Author : Bantam Bowker
File Size : 60.16 MB
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High Performance I O and Its Implication to Computer Architecture

Author : George G. Gorbatenko
File Size : 58.86 MB
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The Publishers Trade List Annual

Author :
File Size : 64.85 MB
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The Bowker Bantam Complete Sourcebook of Personal Computing

Author :
File Size : 30.28 MB
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Energy Primer Solar Water Wind and Biofuels

Author : Richard Merrill
File Size : 48.40 MB
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Deep Learning for Computer Architects

Author : Brandon Reagen
File Size : 74.46 MB
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Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

Who s who in Technology

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Computing Report for the Scientist and Engineer

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File Size : 62.70 MB
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Library Literature Information Science

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An index to library and information science literature.

Annual Report Office of the Chief Scientist

Author : United States. National Park Service. Office of the Chief Scientist
File Size : 79.52 MB
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