Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning

by Ivan Marin, Ankit Shukla, Sarang VK

Overview

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.

• Get a hands-on, fast-paced introduction to the Python data science stack

• Explore ways to create useful metrics and statistics from large datasets

• Create detailed analysis reports with real-world data

Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.

The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools.

By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.

• Use Python to read and transform data into different formats

• Generate basic statistics and metrics using data on disk

• Work with computing tasks distributed over a cluster

• Convert data from various sources into storage or querying formats

• Prepare data for statistical analysis, visualization, and machine learning

• Present data in the form of effective visuals

Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.

Product Details

ISBN-13:	9781789950731
Publisher:	Packt Publishing
Publication date:	04/10/2019
Sold by:	Barnes & Noble
Format:	eBook
Pages:	276
File size:	15 MB
Note:	This product may take a few minutes to download.

About the Author

Ivan Marin is a systems architect and data scientist working at Daitan Group, a Campinas-based software company. He designs big data systems for large volumes of data and implements machine learning pipelines end to end using Python and Spark. He is also an active organizer of data science, machine learning, and Python in Sao Paulo, and has given Python for data science courses at university level.


Ankit Shukla is a data scientist working with World Wide Technology, a leading US-based technology solution provider, where he develops and deploys machine learning and artificial intelligence solutions to solve business problems and create actual dollar value for clients. He is also part of the company's R&D initiative, which is responsible for producing intellectual property, building capabilities in new areas, and publishing cutting-edge research in corporate white papers. Besides tinkering with AI/ML models, he likes to read and is a big-time foodie.


Sarang VK is a lead data scientist at StraitsBridge Advisors, where his responsibilities include requirement gathering, solutioning, development, and productization of scalable machine learning, artificial intelligence, and analytical solutions using open source technologies. Alongside this, he supports pre-sales and competency.

Table of Contents

Table of Contents

1. The Python Data Science Stack
2. Statistical Visualizations
3. Working with Big Data Frameworks
4. Diving Deeper with Spark
5. Handling Missing Values and Correlation Analysis
6. Exploratory Data Analysis
7. Reproducibility in Big Data Analysis
8. Creating a Full Analysis Report