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Big Data Analysis with Python
Big Data Analysis with Python

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

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Big Data Analysis with Python

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Key benefits

  • 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

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.

Who is this book 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.

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
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 10, 2019
Length: 276 pages
Edition : 1st
Language : English
ISBN-13 : 9781789955286
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Product Details

Publication date : Apr 10, 2019
Length: 276 pages
Edition : 1st
Language : English
ISBN-13 : 9781789955286
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


Stars icon
Total 77.97
Hands-On Big Data Analytics with PySpark
€19.99
Big Data Analysis with Python
€24.99
Data Wrangling with Python
€32.99
Total 77.97 Stars icon

Table of Contents

8 Chapters
The Python Data Science Stack Chevron down icon Chevron up icon
Statistical Visualizations Chevron down icon Chevron up icon
Working with Big Data Frameworks Chevron down icon Chevron up icon
Diving Deeper with Spark Chevron down icon Chevron up icon
Handling Missing Values and Correlation Analysis Chevron down icon Chevron up icon
Exploratory Data Analysis Chevron down icon Chevron up icon
Reproducibility in Big Data Analysis Chevron down icon Chevron up icon
Creating a Full Analysis Report Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
(1 Ratings)
5 star 0%
4 star 0%
3 star 0%
2 star 0%
1 star 100%
RLKurtz Jul 14, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This book is a broken mess.The sections on theory, for example the discussion on the which graph to use where in chapter 2, are fine.But then, suddenly, you are asked to do activities by yourself which the textbook hasn't prepared you for and you have to resort to the appendices to see how it should be done. Sometimes (e.g. activity 7) examples just doesn't work - the code is completely broken and the book doesn't prepare you for how to resolve it. No amount of googling is able to put you on the right track either. Exercise 23 just ends in a "type error" as lambda takes 0 positional arguments but 1 was given which, if the forums are to be believed, is because the function is not longer supported by Python. Next your are doing exercises and can follow along the examples in the chapter and all is fine again, but then functionality is introduced which isn't explained and you end up parroting the text. The text makes you install Hadoop, which with the problem solving took me 3 hours, and then moves unceremoniously on to Spark, which with the errors in the text is its own can of worms without ever going into Hadoop at all.This books just comes across as poorly edited and with little to none quality control resulting in frustration. Give it a pass.
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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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