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Big Data Fundamentals

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Big Data Fundamentals

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Author : Thomas Erl
language : en
Publisher: Prentice Hall
Release Date : 2015-12-29

Big Data Fundamentals written by Thomas Erl and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-29 with Computers categories.

“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning

Fundamentals Of Data Science

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Author : Sanjeev J. Wagh
language : en
Publisher: CRC Press
Release Date : 2021-09-26

Fundamentals Of Data Science written by Sanjeev J. Wagh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-26 with Business & Economics categories.

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.

The Fundamentals Of Data Science Big Data Deep Learning And Machine Learning What You Need To Know About Data Science And Why It Matters

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Author : Vlad Sozonov
language : en
Publisher: Vinco Publishing
Release Date : 2019-11-21

The Fundamentals Of Data Science Big Data Deep Learning And Machine Learning What You Need To Know About Data Science And Why It Matters written by Vlad Sozonov and has been published by Vinco Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-21 with Computers categories.

Data science is no easy term to define. While there are many definitions available that point out its statistical or logical aspects, others focus on its machine learning impacts. Today only, get this Amazon book for just $19.99 for a limited time. Regularly priced at $35.99. The truth is, data science is a process that requires an understanding of multiple fields, methods, techniques, and more. Data science cannot be easily labeled because, when applied, it looks different to each person, business, or organization utilizing it. While the term may not be easy to define, what it is used for, can be used for, and approaches to it can be more easily understood. And that is precisely what this book aims to do. Scroll Up & Click to Buy Now! Here Is A Preview Of What You'll Discover...In this step-by-step book: This book will not only thoroughly go over all the skills, people, and steps involved in data science, it will also look closely at: ● What big data is and how data science came from it. ● How data has evolved, resulting in new methods for understanding it. ● How data science influenced artificial intelligence. ● How data science is used in machine learning and deep learning. ● How data science revolutionizes the way we train machines and set up neural networks. Data science, big data, machine learning, and deep learning tend to intimidate people. Many believe it is too complicated or technology-centered for them to break into these fields. This book is designed to simplify these complex areas in a way that anyone can understand the fundamentals. Whether you are just hearing about data science, are a student studying it in college, or looking to expand your career, this book has something to offer every type of data enthusiast. Order your copy today! Take action right away by purchase this book "The Fundamentals of Data Science: Big Data, Deep Learning, and Machine Learning: What you need to know about data science and why it matters.", for a limited time discount of only $19.99! Hurry Up!! Tags: ● data science quick ● data science strategy ● data science trading ● data science journal ● insight data science ● data science salary ● data science jobs ● data science espanol ● data science case study ● data science beginner guide

Mathematical Foundations Of Big Data Analytics

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Author : Vladimir Shikhman
language : en
Publisher: Springer Nature
Release Date : 2021-02-11

Mathematical Foundations Of Big Data Analytics written by Vladimir Shikhman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-11 with Computers categories.

In this textbook, basic mathematical models used in Big Data Analytics are presented and application-oriented references to relevant practical issues are made. Necessary mathematical tools are examined and applied to current problems of data analysis, such as brand loyalty, portfolio selection, credit investigation, quality control, product clustering, asset pricing etc. – mainly in an economic context. In addition, we discuss interdisciplinary applications to biology, linguistics, sociology, electrical engineering, computer science and artificial intelligence. For the models, we make use of a wide range of mathematics – from basic disciplines of numerical linear algebra, statistics and optimization to more specialized game, graph and even complexity theories. By doing so, we cover all relevant techniques commonly used in Big Data Analytics.Each chapter starts with a concrete practical problem whose primary aim is to motivate the study of a particular Big Data Analytics technique. Next, mathematical results follow – including important definitions, auxiliary statements and conclusions arising. Case-studies help to deepen the acquired knowledge by applying it in an interdisciplinary context. Exercises serve to improve understanding of the underlying theory. Complete solutions for exercises can be consulted by the interested reader at the end of the textbook; for some which have to be solved numerically, we provide descriptions of algorithms in Python code as supplementary material.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.

Fundamentals Of Clinical Data Science

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Author : Pieter Kubben
language : en
Publisher: Springer
Release Date : 2018-12-21

Fundamentals Of Clinical Data Science written by Pieter Kubben and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-21 with Medical categories.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Machine Learning And Big Data With Kdb Q

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Author : Jan Novotny
language : en
Publisher: John Wiley & Sons
Release Date : 2019-12-31

Machine Learning And Big Data With Kdb Q written by Jan Novotny and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-31 with Business & Economics categories.

Upgrade your programming language to more effectively handle high-frequency data Machine Learning and Big Data with KDB+/Q offers quants, programmers and algorithmic traders a practical entry into the powerful but non-intuitive kdb+ database and q programming language. Ideally designed to handle the speed and volume of high-frequency financial data at sell- and buy-side institutions, these tools have become the de facto standard; this book provides the foundational knowledge practitioners need to work effectively with this rapidly-evolving approach to analytical trading. The discussion follows the natural progression of working strategy development to allow hands-on learning in a familiar sphere, illustrating the contrast of efficiency and capability between the q language and other programming approaches. Rather than an all-encompassing “bible”-type reference, this book is designed with a focus on real-world practicality to help you quickly get up to speed and become productive with the language. Understand why kdb+/q is the ideal solution for high-frequency data Delve into “meat” of q programming to solve practical economic problems Perform everyday operations including basic regressions, cointegration, volatility estimation, modelling and more Learn advanced techniques from market impact and microstructure analyses to machine learning techniques including neural networks The kdb+ database and its underlying programming language q offer unprecedented speed and capability. As trading algorithms and financial models grow ever more complex against the markets they seek to predict, they encompass an ever-larger swath of data – more variables, more metrics, more responsiveness and altogether more “moving parts.” Traditional programming languages are increasingly failing to accommodate the growing speed and volume of data, and lack the necessary flexibility that cutting-edge financial modelling demands. Machine Learning and Big Data with KDB+/Q opens up the technology and flattens the learning curve to help you quickly adopt a more effective set of tools.

Data Intensive Systems

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Author : Tomasz Wiktorski
language : en
Publisher: Springer
Release Date : 2019-01-01

Data Intensive Systems written by Tomasz Wiktorski and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-01 with Computers categories.

Data-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before continuing with independent work and the more advanced technical reference literature that dominates the current landscape. The material in the book is structured following a problem-based approach. This means that the content in the chapters is focused on developing solutions to simplified, but still realistic problems using data-intensive technologies and approaches. The reader follows one reference scenario through the whole book, that uses an open Apache dataset. The origins of this volume are in lectures from a master’s course in Data-intensive Systems, given at the University of Stavanger. Some chapters were also a base for guest lectures at Purdue University and Lodz University of Technology.

Big Data

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Author : James Warren
language : en
Publisher: Simon and Schuster
Release Date : 2015-04-29

Big Data written by James Warren and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-29 with Computers categories.

Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth

The Fundamentals Of Data Science Big Data Deep Learning And Machine Learning What You Need To Know About Data Science And Why It Matters

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Author : Vlad Sozonov
language : en
Publisher: Vinco Publishing
Release Date : 2019-11-21

Big Data

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Author : Hrushikesha Mohanty
language : en
Publisher: Springer
Release Date : 2015-06-29

Big Data written by Hrushikesha Mohanty and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-29 with Computers categories.

This book is a collection of chapters written by experts on various aspects of big data. The book aims to explain what big data is and how it is stored and used. The book starts from the fundamentals and builds up from there. It is intended to serve as a review of the state-of-the-practice in the field of big data handling. The traditional framework of relational databases can no longer provide appropriate solutions for handling big data and making it available and useful to users scattered around the globe. The study of big data covers a wide range of issues including management of heterogeneous data, big data frameworks, change management, finding patterns in data usage and evolution, data as a service, service-generated data, service management, privacy and security. All of these aspects are touched upon in this book. It also discusses big data applications in different domains. The book will prove useful to students, researchers, and practicing database and networking engineers.

Big Data Fundamentals

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