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A Hands On Introduction To Big Data Analytics

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A Hands On Introduction To Big Data Analytics

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Author : Funmi Obembe
language : en
Publisher: SAGE Publications Limited
Release Date : 2024-02-23

A Hands On Introduction To Big Data Analytics written by Funmi Obembe and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-23 with Business & Economics categories.

This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data professional. It complements theoretical foundations with an emphasis on the application of big data analytics, illustrated by real-life examples and datasets. Containing comprehensive coverage of all the key topics in this area, this book uses open-source technologies and examples in Python and Apache Spark. Learning features include: - Ethics by Design encourages you to consider data ethics at every stage. - Industry Insights facilitate a deeper understanding of the link between what you are studying and how it is applied in industry. - Datasets, questions, and exercises give you the opportunity to apply your learning. Dr Funmi Obembe is the Head of Technology at the Faculty of Arts, Science and Technology, University of Northampton. Dr Ofer Engel is a Data Scientist at the University of Groningen.

Big Data Science Analytics

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Author : Arshdeep Bahga
language : en
Publisher: Vpt
Release Date : 2016-04-15

Big Data Science Analytics written by Arshdeep Bahga and has been published by Vpt this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-15 with categories.

We are living in the dawn of what has been termed as the "Fourth Industrial Revolution," which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. We have written this textbook, as part of our expanding "A Hands-On Approach"(TM) series, to meet this need at colleges and universities, and also for big data service providers who may be interested in offering a broader perspective of this emerging field to accompany their customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks, with examples in Python. We describe Publish-Subscribe messaging frameworks (Kafka & Kinesis), Source-Sink connectors (Flume), Database Connectors (Sqoop), Messaging Queues (RabbitMQ, ZeroMQ, RestMQ, Amazon SQS) and custom REST, WebSocket and MQTT-based connectors. The reader is introduced to data storage, batch and real-time analysis, and interactive querying frameworks including HDFS, Hadoop, MapReduce, YARN, Pig, Oozie, Spark, Solr, HBase, Storm, Spark Streaming, Spark SQL, Hive, Amazon Redshift and Google BigQuery. Also described are serving databases (MySQL, Amazon DynamoDB, Cassandra, MongoDB) and the Django Python web framework. Part III introduces the reader to various machine learning algorithms with examples using the Spark MLlib and H2O frameworks, and visualizations using frameworks such as Lightning, Pygal and Seaborn.

Practical Big Data Analytics

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Author : Nataraj Dasgupta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-15

Practical Big Data Analytics written by Nataraj Dasgupta 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 2018-01-15 with Computers categories.

Get command of your organizational Big Data using the power of data science and analytics Key Features A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data Book Description Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book. What you will learn - Get a 360-degree view into the world of Big Data, data science and machine learning - Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives - Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R - Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions - Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications - Understand corporate strategies for successful Big Data and data science projects - Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies Who this book is for The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.

A Hands On Introduction To Data Science

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Author : Chirag Shah
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-02

A Hands On Introduction To Data Science written by Chirag Shah and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Business & Economics categories.

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

A Hands On Introduction To Big Data Analytics

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Author : Funmi Obembe
language : en
Publisher: SAGE Publications Limited
Release Date : 2024-02-24

Python Data Science

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Author : Computer Programming Academy
language : en
Publisher:
Release Date : 2020-02-10

Python Data Science written by Computer Programming Academy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-10 with categories.

Would you like to learn to use Python extracting meaningful insight from data to grow your business but you reckon it will be too complex? Or perhaps you want to know how to analyze data to solve simple domestic issues but you don't know how to do it? Here's the deal... As a beginner you will be probably afraid that programming is difficult... Learning data analysis and data mining can take months, and the possibility to give up before mastering them could be high. So, if you have a project to develop you could think on hiring a professional analyst to shorten the time. This may seem like a good solution but it is certainly very expensive and if the analyst you chose doesn't perform a proper job you still have to pay for it. The best solution is a complete programming manual with hands-on projects and practical exercises. Computer Programming Academy structured this guide as a course with seven chapters for seven days and studied special exercises for each section to apply what you learned step-by-step. This protocol, tested on both total beginners and people who were already familiar with coding, takes advantage of the principle of diving, concentrating learning in one week. The result of this method has been one for both categories of students: the content of the course was learned faster and remembered longer respect the average. Inside this book, you will go through a first section in which fundamental and basic notions of data science are discussed, to get to the next chapters crafted specifically to help you learn all the advance data analysis concepts required to produce valuable outcomes from a large volume of data. In the detail, you will learn: The importance of data science technologies in our daily lives What are the most common mistakes to avoid when you start dealing with Python for data science The 5 stages of the data science lifecycle at the basis of most used applications 3 important actions required to gain insights from big data What are the advantages of the data mining process in resolving real world problems The data analysis tools that will make your life easier 3 key frameworks that you have to know to transform unstructured and unorganized data in significant insight How to download and use the main Python based data analysis libraries A simple method to implement predictive analytics to resolve a business issue in less than 7 days A proven strategy to develop predictive models to analyze customers' behavior Exercises and quizzes at the end of every chapter to review immediately what you've learned Extra content that you will appreciate as curious technology enthusiast Why is this book different for? Most of the books on the market only take a brief look into data science, showing some of the topics but never going deep concretely. The best way to learn data analysis and data mining is by doing and with this manual you will work through applicable projects in order to solidify your knowledge and obtain a huge sense of achievement. This is what this guide offers to you, even if you're completely new to programming in 2020 or you are looking to widen your skills as programmer. Would You Like To Know More? Scroll up to the top of the page and select the BUY NOW button. The key to become a Python master is one click away!

Big Data Science Analytics

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Author : Arshdeep Bahga
language : en
Publisher: Vpt
Release Date : 2016-04-15

Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. We have written this textbook to meet this need at colleges and universities, and also for big data service providers.

Network Data Analytics

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Author : K. G. Srinivasa
language : en
Publisher: Springer
Release Date : 2018-04-26

Network Data Analytics written by K. G. Srinivasa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-26 with Computers categories.

In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.

Introduction To Big Data Infrastructure And Networking Considerations

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Author : Shoban Babu Sriramoju
language : en
Publisher: Horizon Books ( A Division of Ignited Minds Edutech P Ltd)
Release Date : 2017-12-01

Introduction To Big Data Infrastructure And Networking Considerations written by Shoban Babu Sriramoju and has been published by Horizon Books ( A Division of Ignited Minds Edutech P Ltd) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-01 with categories.

Big data is certainly one of the biggest buzz phrases in IT today. Combined with virtualization and cloud computing, big data is a technological capability that will force data centers to significantly transform and evolve within the next five years. Similar to virtualization, big data infrastructure is unique and can create an architectural upheaval in the way systems, storage, and software infrastructure are connected and managed. Unlike previous business analytics solutions, the real-time capability of new big data solutions can provide mission critical business intelligence that can change the shape and speed of enterprise decision making forever. Hence, the way in which IT infrastructure is connected and distributed warrants a fresh and critical analysis.

Big Data

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Author : Saswat Sarangi
language : en
Publisher: Taylor & Francis
Release Date : 2019-09-09

Big Data written by Saswat Sarangi and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-09 with Social Science categories.

Big Data is everywhere. It shapes our lives in more ways than we know and understand. This comprehensive introduction unravels the complex terabytes that will continue to shape our lives in ways imagined and unimagined. Drawing on case studies like Amazon, Facebook, the FIFA World Cup and the Aadhaar scheme, this book looks at how Big Data is changing the way we behave, consume and respond to situations in the digital age. It looks at how Big Data has the potential to transform disaster management and healthcare, as well as prove to be authoritarian and exploitative in the wrong hands. The latest offering from the authors of Artificial Intelligence: Evolution, Ethics and Public Policy, this accessibly written volume is essential for the researcher in science and technology studies, media and culture studies, public policy and digital humanities, as well as being a beacon for the general reader to make sense of the digital age.

A Hands On Introduction To Big Data Analytics

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