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Machine Theory

Introducing Data Science: Big Data, Machine Learning, and by Davy Cielen, Arno Meysman, Mohamed Ali

By Davy Cielen, Arno Meysman, Mohamed Ali

Summary

Introducing information Science teaches you ways to complete the elemental projects that occupy information scientists. utilizing the Python language and customary Python libraries, you will event firsthand the demanding situations of facing facts at scale and achieve an exceptional beginning in information science.

Purchase of the print publication contains a unfastened booklet in PDF, Kindle, and ePub codecs from Manning Publications.

About the Technology

Many businesses want builders with information technology talents to paintings on initiatives starting from social media advertising and marketing to computing device studying. studying what you must discover ways to commence a occupation as an information scientist can appear bewildering. This booklet is designed that can assist you get started.

About the Book

Introducing information ScienceIntroducing facts technological know-how explains important facts technology options and teaches you the way to complete the elemental projects that occupy info scientists. You’ll discover info visualization, graph databases, using NoSQL, and the knowledge technology procedure. You’ll use the Python language and customary Python libraries as you event firsthand the demanding situations of facing information at scale. become aware of how Python enables you to achieve insights from information units so colossal that they should be saved on a number of machines, or from facts relocating so speedy that no unmarried desktop can deal with it. This booklet offers hands-on event with the preferred Python information technology libraries, Scikit-learn and StatsModels. After studying this publication, you’ll have the forged origin you must begin a occupation in info technological know-how.

What’s Inside

  • Handling huge data
  • Introduction to computing device learning
  • Using Python to paintings with data
  • Writing facts technology algorithms

About the Reader

This publication assumes you are cozy studying code in Python or an analogous language, resembling C, Ruby, or JavaScript. No earlier adventure with facts technology is required.

About the Authors

Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and coping with companions of Optimately and Maiton, the place they specialise in constructing facts technological know-how tasks and ideas in numerous sectors.

Table of Contents

  1. Data technology in an important information world
  2. The info technology process
  3. Machine learning
  4. Handling huge information on a unmarried computer
  5. First steps in enormous data
  6. Join the NoSQL movement
  7. The upward thrust of graph databases
  8. Text mining and textual content analytics
  9. Data visualization to the top user

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Additional resources for Introducing Data Science: Big Data, Machine Learning, and more, using Python tools

Sample text

Data can also be delivered by third-party companies and takes many forms ranging from Excel spreadsheets to different types of databases. 3 Data preparation Data collection is an error-prone process; in this phase you enhance the quality of the data and prepare it for use in subsequent steps. This phase consists of three subphases: data cleansing removes false values from a data source and inconsistencies across data sources, data integration enriches data sources by combining information from multiple data sources, and data transformation ensures that the data is in a suitable format for use in your models.

7 Graph databases—Not every problem is best stored in a table. Particular problems are more naturally translated into graph theory and stored in graph databases. A classic example of this is a social network. Scheduling tools Scheduling tools help you automate repetitive tasks and trigger jobs based on events such as adding a new file to a folder. These are similar to tools such as CRON on Linux but are specifically developed for big data. You can use them, for instance, to start a MapReduce task whenever a new dataset is available in a directory.

Later on in this book you’ll see how Juju eases the installation of Hadoop on multiple machines. We’ll use a small data set of job salary data to run our first sample, but querying a large data set of billions of rows would be equally easy. The query language will seem like SQL, but behind the scenes a MapReduce job will run and produce a straightforward table of results, which can then be turned into a bar graph. 7. 7 The end result: the average salary by job description To get up and running as fast as possible we use a Hortonworks Sandbox inside VirtualBox.

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