Book Image

Big Data Analysis with Python

By : Ivan Marin, Ankit Shukla, Sarang VK
Book Image

Big Data Analysis with Python

By: Ivan Marin, Ankit Shukla, Sarang VK

Overview of this book

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.
Table of Contents (11 chapters)
Big Data Analysis with Python
Preface

Introduction


One of the most important stages, and the initial step of a data science project, is understanding and defining a business problem. However, this cannot be a mere reiteration of the existing problem as a statement or a written report. To investigate a business problem in detail and define its purview, we can either use the existing business metrics to explain the patterns related to it or quantify and analyze the historical data and generate new metrics. Such identified metrics are the Key Performance Indicators (KPIs) that measure the problem at hand and convey to business stakeholders the impact of a problem.

This chapter is all about understanding and defining a business problem, identifying key metrics related to it, and using these identified and generated KPIs through pandas and similar libraries for descriptive analytics. The chapter also covers how to plan a data science project through a structured approach and methodology, concluding with how to represent a problem...