Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning

Buy

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

Key Features

  • 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

Book Description

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.

What you will learn

  • 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

Who this book is for

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.

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
(HTML tags aren't allowed.)

Building Digital Experience Platforms: A Guide to Developing Next-Generation Enterprise Applications
Building Digital Experience Platforms: A Guide to Developing Next-Generation Enterprise Applications

Use digital experience platforms (DXP) to improve your development productivity and release timelines. Leverage the pre-integrated feature sets of DXPs in your organization's digital transformation journey to quickly develop a personalized, secure, and robust enterprise platform.

In this book the authors examine...

Python for Data Science For Dummies (For Dummies (Computer/Tech))
Python for Data Science For Dummies (For Dummies (Computer/Tech))

The fast and easy way to learn Python programming and statistics

Python is a general-purpose programming language created in the late 1980s?and named after Monty Python?that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the...

Linear Programming: Foundations and Extensions (International Series in Operations Research & Management Science)
Linear Programming: Foundations and Extensions (International Series in Operations Research & Management Science)
Linear Programming: Foundations and Extensions is an introduction to the field of optimization. The book emphasizes constrained optimization, beginning with a substantial treatment of linear programming, and proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex optimization.

The book is...


Computational Intelligence in Optimization: Applications and Implementations (Adaptation, Learning, and Optimization)
Computational Intelligence in Optimization: Applications and Implementations (Adaptation, Learning, and Optimization)

Optimization is an integral part to science and engineering. Most real-world applications involve complex optimization processes, which are difficult to solve without advanced computational tools. With the increasing challenges of fulfilling optimization goals of current applications there is a strong drive to advance the development of...

Applied and Numerical Partial Differential Equations: Scientific Computing in Simulation, Optimization and Control in a Multidisciplinary Context
Applied and Numerical Partial Differential Equations: Scientific Computing in Simulation, Optimization and Control in a Multidisciplinary Context
The present volume is comprised of contributions solicited from invitees to conferences held at the University of Houston, University of Jyvaskyla, and XT an Jiaotong University honoring the 70th birthday of Professor Roland Glowinski. Although scientists convened on three different continents, the editors prefer to view the meetings as...
Python Data Visualization Cookbook
Python Data Visualization Cookbook

As a developer with knowledge of Python you are already in a great position to start using data visualization. This superb cookbook shows you how in plain language and practical recipes, culminating with 3D animations.

Overview

  • Learn how to set up an optimal Python environment for data visualization
  • ...
©2020 LearnIT (support@pdfchm.net) - Privacy Policy