› › A Hands On Introduction To Big Data Analytics

A Hands-on Introduction to Big Data Analytics

Author : Funmi Obembe

Publisher : SAGE Publications Limited

Page : 393 pages

File Size : 38,30 MB

Release : 2024-02-24

Category : Business & Economics

ISBN : 1529615925

Get eBook

Book Description

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

Author : Arshdeep Bahga

Publisher : Vpt

Page : 544 pages

File Size : 16,12 MB

Release : 2016-04-15

Category : Computers

ISBN : 9780996025546

Get eBook

Book Description

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.



A Hands-On Introduction to Data Science

Author : Chirag Shah

Publisher : Cambridge University Press

Page : 459 pages

File Size : 42,19 MB

Release : 2020-04-02

Category : Business & Economics

ISBN : 1108472443

Get eBook

Book Description

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

Author : Funmi Obembe

Publisher : SAGE Publications Limited

Page : 415 pages

File Size : 35,22 MB

Release : 2024-02-23

Category : Business & Economics

ISBN : 1529615909

Get eBook

Book Description

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.



Hands-On Big Data Analytics with PySpark

Author : Rudy Lai

Publisher : Packt Publishing Ltd

Page : 172 pages

File Size : 46,2 MB

Release : 2019-03-29

Category : Computers

ISBN : 1838648836

Get eBook

Book Description

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs Key FeaturesWork with large amounts of agile data using distributed datasets and in-memory cachingSource data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3Employ the easy-to-use PySpark API to deploy big data Analytics for productionBook Description Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively. What you will learnGet practical big data experience while working on messy datasetsAnalyze patterns with Spark SQL to improve your business intelligenceUse PySpark's interactive shell to speed up development timeCreate highly concurrent Spark programs by leveraging immutabilityDiscover ways to avoid the most expensive operation in the Spark API: the shuffle operationRe-design your jobs to use reduceByKey instead of groupByCreate robust processing pipelines by testing Apache Spark jobsWho this book is for This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.



Python Data Science

Author : Computer Programming Academy

Publisher :

Page : 202 pages

File Size : 22,9 MB

Release : 2020-11-10

Category :

ISBN : 9781914185106

Get eBook

Book Description

Inside this book you will find all the basic notions to start with Python and all the programming concepts to implement predictive analytics. With our proven strategies you will write efficient Python codes in less than a week!



Frank Kane's Taming Big Data with Apache Spark and Python

Author : Frank Kane

Publisher : Packt Publishing Ltd

Page : 289 pages

File Size : 19,35 MB

Release : 2017-06-30

Category : Computers

ISBN : 1787288307

Get eBook

Book Description

Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary in complexity, and you can move through them at your own pace.



Hands-On Big Data Modeling

Author : James Lee

Publisher : Packt Publishing Ltd

Page : 293 pages

File Size : 20,33 MB

Release : 2018-11-30

Category : Computers

ISBN : 1788626087

Get eBook

Book Description

Solve all big data problems by learning how to create efficient data models Key FeaturesCreate effective models that get the most out of big dataApply your knowledge to datasets from Twitter and weather data to learn big dataTackle different data modeling challenges with expert techniques presented in this bookBook 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 learnGet insights into big data and discover various data modelsExplore conceptual, logical, and big data modelsUnderstand how to model data containing different file typesRun through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modelingCreate data models such as Graph Data and Vector SpaceModel structured and unstructured data using Python and RWho 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.



Big Data Analytics with Java

Author : Rajat Mehta

Publisher : Packt Publishing Ltd

Page : 419 pages

File Size : 10,25 MB

Release : 2017-07-31

Category : Computers

ISBN : 1787282198

Get eBook

Book Description

Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.



Practical Big Data Analytics

Author : Nataraj Dasgupta

Publisher : Packt Publishing Ltd

Page : 402 pages

File Size : 31,33 MB

Release : 2018-01-15

Category : Computers

ISBN : 1783554401

Get eBook

Book Description

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.