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Big Data Visualization

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Big Data Visualization, 9781785281945 (1785281941), Packt Publishing, 2017
Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf Improve your decision-making by visualizing your big data the right way Who This Book Is For This book is for data analysts or those with a basic knowledge of big data analysis who want to learn big data visualization in order to make their analysis more useful. You need sufficient knowledge of big data platform tools such as Hadoop and also some experience with programming languages such as R. This book will be great for those who are familiar with conventional data visualizations and now want to widen their horizon by exploring big data visualizations. What You Will Learn Understand how basic analytics is affected by big data Deep dive into effective and efficient ways of visualizing big data Get to know various approaches (using various technologies) to address the challenges of visualizing big data Comprehend the concepts and models used to visualize big data Know how to visualize big data in real time and for different use cases Understand how to integrate popular dashboard visualization tools such as Splunk and Tableau Get to know the value and process of integrating visual big data with BI tools such as Tableau Make sense of the visualization options for big data, based upon the best suited visualization techniques for big data In Detail When it comes to big data, regular data visualization tools with basic features become insufficient. This book covers the concepts and models used to visualize big data, with a focus on efficient visualizations. This book works around big data visualizations and the challenges around visualizing big data and address
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Python Data Analysis
Python Data Analysis

Key Features

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  • Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
  • An easy-to-follow guide with realistic examples that are frequently used in real-world data...
Python Web Scraping: Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python, 2nd Edition
Python Web Scraping: Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python, 2nd Edition

Successfully scrape data from any website with the power of Python 3.x

Key Features

  • A hands-on guide to web scraping using Python with solutions to real-world problems
  • Create a number of different web scrapers in Python to extract information
  • This book...
Practical Data Wrangling: Expert techniques for transforming your raw data into a valuable source for analytics
Practical Data Wrangling: Expert techniques for transforming your raw data into a valuable source for analytics

Key Features

  • This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way
  • Work with different types of datasets, and reshape the layout of your data to make it easier for analysis
  • Get simple examples and real-life data...

Python Data Science Handbook: Essential Tools for Working with Data
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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib,...

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Hacking: THE BLUEPRINT A Beginners Guide To Ethical Computer Hacking (CyberPunk Blueprint Series)
Ever wonder how easy it is to hack into someone’s bank account info while surfing the net at your local Starbucks? Learning how to hack is one of the best preventative measures you can take when protecting yourself from other hackers. Hacking is often misunderstood and that is for good reason. Many people use hacking as...
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

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