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  • Book Overview & Buying Spark for Data Science
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Spark for Data Science

Spark for Data Science

By : Srinivas Duvvuri, Bikramaditya Singhal
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Spark for Data Science

Spark for Data Science

By: Srinivas Duvvuri, Bikramaditya Singhal
Buy this Book

Overview of this book

This is the era of Big Data. The words ‘Big Data’ implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Big Data and Data Science – An Introduction
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1. Big Data and Data Science – An Introduction
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Big data overview
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Challenges with big data analytics
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Evolution of big data analytics
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Spark for data analytics
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The Spark stack
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Summary
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References
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2. The Spark Programming Model
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2. The Spark Programming Model
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The programming paradigm
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The Spark engine
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The RDD API
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RDD operations
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Summary
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References
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3. Introduction to DataFrames
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3. Introduction to DataFrames
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Why DataFrames?
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Spark SQL
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The DataFrame API
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Creating DataFrames
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DataFrame operations
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Summary
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References
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4. Unified Data Access
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4. Unified Data Access
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Data abstractions in Apache Spark
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Datasets
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Spark SQL
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Structured Streaming
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Continuous applications
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Summary
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References
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5. Data Analysis on Spark
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5. Data Analysis on Spark
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Data analytics life cycle
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Data acquisition
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Data preparation
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Basics of statistics
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Descriptive statistics
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Inferential statistics
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Summary
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References
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6. Machine Learning
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6. Machine Learning
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Introduction
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MLlib and the Pipeline API
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Introduction to machine learning
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Regression methods
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Classification methods
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Linear Support Vector Machines (SVM)
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Training an SVM
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Decision trees
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Ensembles
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Multilayer perceptron classifier
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Clustering techniques
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Summary
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References
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7. Extending Spark with SparkR
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7. Extending Spark with SparkR
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SparkR basics
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Advantages and limitations
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Programming with SparkR
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SparkR DataFrames
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Machine learning
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Summary
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References
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8. Analyzing Unstructured Data
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8. Analyzing Unstructured Data
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Sources of unstructured data
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Processing unstructured data
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Text classification
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Text clustering
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Dimensionality reduction
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Singular Value Decomposition
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Summary
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References:
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9. Visualizing Big Data
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9. Visualizing Big Data
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Why visualize data?
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Data visualization tools
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Data visualization techniques
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Summary
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References
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10. Putting It All Together
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10. Putting It All Together
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A quick recap
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Introducing a case study
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The business problem
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Data acquisition and data cleansing
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Developing the hypothesis
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Data exploration
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Data preparation
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Model building
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Data visualization
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Communicating the results to business users
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Summary
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References
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11. Building Data Science Applications
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11. Building Data Science Applications
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Scope of development
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The Scala advantage
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Spark development status
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The big data trends
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Summary
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References

Summary

In this chapter, we briefly covered what big data is all about. We then discussed the computational and analytical challenges involved in big data analytics. Later, we looked at how the analytics space in the context of big data has evolved over a period of time and what the trend has been. We also covered how Spark addressed most of the big data analytics challenges and became a general-purpose unified analytics platform for data science as well as parallel computation. At the end of this chapter, we just gave you a heads-up on the Spark stack and its components.

In the next chapter, we will learn about the Spark programming model. We will take a deep dive into the basic building block of Spark, which is the RDD. Also, we will learn how to program with the RDD API on Scala and Python.

CONTINUE READING
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Tech Concepts
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Programming languages
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Spark for Data Science
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