Practical Big Data Analytics

Get command of your organizational Big Data using the power of data science and analytics
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Practical Big Data Analytics

Nataraj Dasgupta

2 customer reviews
Get command of your organizational Big Data using the power of data science and analytics
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Book Details

ISBN 139781783554393
Paperback412 pages

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.

Table of Contents

Chapter 1: Too Big or Not Too Big
What is big data?
Why we are talking about big data now if data has always existed
Types of Big Data
Sources of big data
When do you know you have a big data problem and where do you start your search for the big data solution?
Summary
Chapter 2: Big Data Mining for the Masses
What is big data mining?
Technical elements of the big data platform
Summary
Chapter 3: The Analytics Toolkit
Components of the Analytics Toolkit
System recommendations
Installing Hadoop
Installing Packt Data Science Box
Installing Spark
Installing R
Installing RStudio
Installing Python
Summary
Chapter 4: Big Data With Hadoop
The fundamentals of Hadoop
The Hadoop ecosystem
Hands-on with CDH
Summary
Chapter 5: Big Data Mining with NoSQL
Why NoSQL?
NoSQL databases
Analyzing Nobel Laureates data with MongoDB
Tracking physician payments with real-world data
The CMS Open Payments Portal
R Shiny platform for developers
Summary
Chapter 6: Spark for Big Data Analytics
The advent of Spark
Spark practicals
Spark exercise - hands-on with Spark (Databricks)
Summary
Chapter 7: An Introduction to Machine Learning Concepts
What is machine learning?
Factors that led to the success of machine learning
Machine learning, statistics, and AI
Categories of machine learning
Subdividing supervised machine learning
Common terminologies in machine learning
The core concepts in machine learning
Leveraging multicore processing in the model
Summary
Chapter 8: Machine Learning Deep Dive
The bias, variance, and regularization properties
The gradient descent and VC Dimension theories
Popular machine learning algorithms
Tutorial - associative rules mining with CMS data
Summary
Chapter 9: Enterprise Data Science
Enterprise data science overview
A roadmap to enterprise analytics success
Data science solutions in the enterprise
Enterprise data science – machine learning and AI
Enterprise infrastructure solutions
Tutorial – using RStudio in the cloud
Summary
Chapter 10: Closing Thoughts on Big Data
Corporate big data and data science strategy
Ethical considerations
Silicon Valley and data science
The human factor
Summary
Chapter 11: External Data Science Resources
Big data resources
NoSQL products
Languages and tools
Creating dashboards
Notebooks
Visualization libraries
Courses on R
Courses on machine learning
Machine learning and deep learning links
Web-based machine learning services
Movies
Machine learning books from Packt
Books for leisure reading

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

Authors

Table of Contents

Chapter 1: Too Big or Not Too Big
What is big data?
Why we are talking about big data now if data has always existed
Types of Big Data
Sources of big data
When do you know you have a big data problem and where do you start your search for the big data solution?
Summary
Chapter 2: Big Data Mining for the Masses
What is big data mining?
Technical elements of the big data platform
Summary
Chapter 3: The Analytics Toolkit
Components of the Analytics Toolkit
System recommendations
Installing Hadoop
Installing Packt Data Science Box
Installing Spark
Installing R
Installing RStudio
Installing Python
Summary
Chapter 4: Big Data With Hadoop
The fundamentals of Hadoop
The Hadoop ecosystem
Hands-on with CDH
Summary
Chapter 5: Big Data Mining with NoSQL
Why NoSQL?
NoSQL databases
Analyzing Nobel Laureates data with MongoDB
Tracking physician payments with real-world data
The CMS Open Payments Portal
R Shiny platform for developers
Summary
Chapter 6: Spark for Big Data Analytics
The advent of Spark
Spark practicals
Spark exercise - hands-on with Spark (Databricks)
Summary
Chapter 7: An Introduction to Machine Learning Concepts
What is machine learning?
Factors that led to the success of machine learning
Machine learning, statistics, and AI
Categories of machine learning
Subdividing supervised machine learning
Common terminologies in machine learning
The core concepts in machine learning
Leveraging multicore processing in the model
Summary
Chapter 8: Machine Learning Deep Dive
The bias, variance, and regularization properties
The gradient descent and VC Dimension theories
Popular machine learning algorithms
Tutorial - associative rules mining with CMS data
Summary
Chapter 9: Enterprise Data Science
Enterprise data science overview
A roadmap to enterprise analytics success
Data science solutions in the enterprise
Enterprise data science – machine learning and AI
Enterprise infrastructure solutions
Tutorial – using RStudio in the cloud
Summary
Chapter 10: Closing Thoughts on Big Data
Corporate big data and data science strategy
Ethical considerations
Silicon Valley and data science
The human factor
Summary
Chapter 11: External Data Science Resources
Big data resources
NoSQL products
Languages and tools
Creating dashboards
Notebooks
Visualization libraries
Courses on R
Courses on machine learning
Machine learning and deep learning links
Web-based machine learning services
Movies
Machine learning books from Packt
Books for leisure reading

Book Details

ISBN 139781783554393
Paperback412 pages
Read More
From 2 reviews

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