Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Big Data Visualization

You're reading from Big Data Visualization Bring scalability and dynamics to your Big Data visualization

Product type Paperback

Published in Feb 2017

Publisher Packt

ISBN-13 9781785281945

Length 304 pages

Edition 1st Edition

Languages

R

Tools

D3

Concepts

Big Data

Author (1):

James D. Miller

View More author details

Table of Contents (9) Chapters

Preface

1. Introduction to Big Data Visualization FREE CHAPTER

2. Access, Speed, and Storage with Hadoop

3. Understanding Your Data Using R

4. Addressing Big Data Quality

5. Displaying Results Using D3

6. Dashboards for Big Data - Tableau

7. Dealing with Outliers Using Python

8. Big Data Operational Intelligence with Splunk

What you need for this book

Most of the tools and technologies used in this book are open source and available for no charge. All of the others offer free trials for evaluation. With this book, and some basic exposure to data analysis (or basic programming concepts) the reader will be able to gain valuable insights to the world of big data visualization!

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $19.99/month. Cancel anytime

Authors (1)

James D. Miller

James D. Miller

James D. Miller is an IBM certified expert, Master Consultant, Application/System Architect with +35 years of applications & system design/development experience across multiple platforms, technologies and data formats, including Big Data. His experience includes IBM Planning Analytics, BI, Web architecture & design, systems analysis, GUI design & testing, Data modeling, design, and development of OLAP, Client/Server, Web & Mainframe applications and systems utilizing: Planning Analytics Workspace (PAW), IBM Watson Analytics, Cognos BI & TM1, Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, MS Visual Basic, VBA, PERL, R, SPLUNK, MS SQL Server, ORACLE, etc. He has authored numerous books, including Implementing Splunk - Second Edition; Mastering Splunk; Hands-On Machine Learning with IBM Watson; IBM Watson Projects; Statistics for Data Science; Mastering Predictive Analytics with R - Second Edition and others. Project areas include those with Data Analytics, Planning Analytics, and FOPM projects, holding various roles from architect, developer, technical and project leader.

See other products by James D. Miller

Other recommended products

Related to this chapter

Statistics for Data Science

Statistics for Data Science

Statistics is an absolute must prerequisite for any task in the area of data science. This is also the most feared hindrance for developers to entering in the data science field. This book will start off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for the statistical computations will be clearly explained along with the logic. You will come across various mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis.

Nov 2017 9h 32m

Statistics for Data Science

Statistics for Data Science

Statistics is an absolute must prerequisite for any task in the area of data science. This is also the most feared hindrance for developers to entering in the data science field. This book will start off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for the statistical computations will be clearly explained along with the logic. You will come across various mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis.

Nov 2017 9h 32m

Statistics for Data Science

Statistics for Data Science

Statistics is an absolute must prerequisite for any task in the area of data science. This is also the most feared hindrance for developers to entering in the data science field. This book will start off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for the statistical computations will be clearly explained along with the logic. You will come across various mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis.

Nov 2017 9h 32m

Statistics for Data Science

Statistics for Data Science

Statistics is an absolute must prerequisite for any task in the area of data science. This is also the most feared hindrance for developers to entering in the data science field. This book will start off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for the statistical computations will be clearly explained along with the logic. You will come across various mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis.

Nov 2017 9h 32m

Statistics for Data Science

Statistics for Data Science

Statistics is an absolute must prerequisite for any task in the area of data science. This is also the most feared hindrance for developers to entering in the data science field. This book will start off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for the statistical computations will be clearly explained along with the logic. You will come across various mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis.

Nov 2017 9h 32m

Splunk 7 Essentials

Splunk 7 Essentials

This book will uncover the new features in Splunk 7 along with the best practices. You will learn to build navigable search operations, perform intuitive statistical analysis and design visually appealing dashboards for your IT infrastructure. With practical scenarios, you'll be able to design cohesive Splunk apps and deploy it to Splunk cloud.

Mar 2018 7h 20m

Splunk 7 Essentials

Splunk 7 Essentials

This book will uncover the new features in Splunk 7 along with the best practices. You will learn to build navigable search operations, perform intuitive statistical analysis and design visually appealing dashboards for your IT infrastructure. With practical scenarios, you'll be able to design cohesive Splunk apps and deploy it to Splunk cloud.

Mar 2018 7h 20m

Splunk 7 Essentials

Splunk 7 Essentials

This book will uncover the new features in Splunk 7 along with the best practices. You will learn to build navigable search operations, perform intuitive statistical analysis and design visually appealing dashboards for your IT infrastructure. With practical scenarios, you'll be able to design cohesive Splunk apps and deploy it to Splunk cloud.

Mar 2018 7h 20m

Splunk 7 Essentials

Splunk 7 Essentials

This book will uncover the new features in Splunk 7 along with the best practices. You will learn to build navigable search operations, perform intuitive statistical analysis and design visually appealing dashboards for your IT infrastructure. With practical scenarios, you'll be able to design cohesive Splunk apps and deploy it to Splunk cloud.

Mar 2018 7h 20m

Splunk 7 Essentials

Splunk 7 Essentials

This book will uncover the new features in Splunk 7 along with the best practices. You will learn to build navigable search operations, perform intuitive statistical analysis and design visually appealing dashboards for your IT infrastructure. With practical scenarios, you'll be able to design cohesive Splunk apps and deploy it to Splunk cloud.

Mar 2018 7h 20m

Splunk 7 Essentials

Splunk 7 Essentials

This book will uncover the new features in Splunk 7 along with the best practices. You will learn to build navigable search operations, perform intuitive statistical analysis and design visually appealing dashboards for your IT infrastructure. With practical scenarios, you'll be able to design cohesive Splunk apps and deploy it to Splunk cloud.

Mar 2018 7h 20m

Splunk 7 Essentials

Splunk 7 Essentials

This book will uncover the new features in Splunk 7 along with the best practices. You will learn to build navigable search operations, perform intuitive statistical analysis and design visually appealing dashboards for your IT infrastructure. With practical scenarios, you'll be able to design cohesive Splunk apps and deploy it to Splunk cloud.

Mar 2018 7h 20m

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m