Download e-book for iPad: Introducing Data Science: Big Data, Machine Learning and by Davy Cielen, Arno Meysman, Mohamed Ali

By Davy Cielen, Arno Meysman, Mohamed Ali

ISBN-10: 1633430030

ISBN-13: 9781633430037

Summary

Introducing information Science teaches you ways to complete the basic 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 a high-quality starting place in info science.

Purchase of the print e-book features 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 to computer studying. learning what you want to learn how to start a profession as an information scientist can appear bewildering. This booklet is designed that will help you get started.

About the Book

Introducing information ScienceIntroducing facts technological know-how explains very important information technology innovations and teaches you the way to complete the elemental projects that occupy facts scientists. You’ll discover facts visualization, graph databases, using NoSQL, and the information technology method. You’ll use the Python language and customary Python libraries as you adventure firsthand the demanding situations of facing facts at scale. observe how Python lets you achieve insights from facts units so immense that they should be saved on a number of machines, or from info relocating so quick that no unmarried laptop can deal with it. This publication promises hands-on event with the most well-liked Python info technology libraries, Scikit-learn and StatsModels. After examining this e-book, you’ll have the cast starting place you must commence a occupation in information technology.

What’s Inside

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

About the Reader

This booklet assumes you are cozy studying code in Python or the same language, equivalent to C, Ruby, or JavaScript. No previous adventure with facts technological know-how is required.

About the Authors

Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and dealing with companions of Optimately and Maiton, the place they specialise in constructing information technological know-how tasks and recommendations in quite a few sectors.

Table of Contents

  1. Data technology in an important information world
  2. The information technology process
  3. Machine learning
  4. Handling huge information on a unmarried computer
  5. First steps in huge 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

Example text

They often require human intervention, and because humans are only human, they make typos or lose their concentration for a second and introduce an error into the chain. But data collected by machines or computers isn’t free from errors either. Errors can arise from human sloppiness, whereas others are due to machine or hardware failure. Examples of errors originating from machines are transmission errors or bugs in the extract, transform, and load phase (ETL). For small data sets you can check every value by hand.

But keep in mind that other types of data sources exist, such as key-value stores, document stores, and so on, which we’ll handle in more appropriate places in the book. THE DIFFERENT WAYS OF COMBINING DATA You can perform two operations to combine information from different data sets. The first operation is joining: enriching an observation from one table with information from another table. The second operation is appending or stacking: adding the observations of one table to those of another table.

In this case the data wasn’t technically wrong but came with unexpected results. Data errors may point to defective equipment, such as broken transmission lines and defective sensors. Data errors can point to bugs in software or in the integration of software that may be critical to the company. While doing a small project at a bank we discovered that two software applications used different local settings. This caused problems with numbers greater than 1,000. 000 meant one, and for the other it meant one thousand.

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


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