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

By Davy Cielen, Arno Meysman, Mohamed Ali

Summary

Introducing info Science teaches you the way to complete the basic projects that occupy facts scientists. utilizing the Python language and customary Python libraries, you will adventure firsthand the demanding situations of facing info at scale and achieve a high-quality beginning in information science.

Purchase of the print booklet features a unfastened e-book in PDF, Kindle, and ePub codecs from Manning Publications.

About the Technology

Many businesses desire builders with facts technology abilities to paintings on initiatives starting from social media advertising to laptop studying. getting to know what you must learn how to commence a occupation as a knowledge scientist can appear bewildering. This publication is designed that can assist you get started.

About the Book

Introducing info ScienceIntroducing information technological know-how explains very important facts technology innovations and teaches you ways to complete the elemental initiatives that occupy info scientists. You’ll discover information visualization, graph databases, using NoSQL, and the knowledge technological know-how procedure. You’ll use the Python language and customary Python libraries as you adventure firsthand the demanding situations of facing information at scale. detect how Python permits you to achieve insights from info units so immense that they should be kept on a number of machines, or from information relocating so speedy that no unmarried laptop can deal with it. This ebook delivers hands-on event with the most well-liked Python information technology libraries, Scikit-learn and StatsModels. After examining this e-book, you’ll have the forged starting place you want to begin a profession in information technological know-how.

What’s Inside

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

About the Reader

This booklet assumes you are cozy studying code in Python or an analogous language, equivalent to C, Ruby, or JavaScript. No previous event with information 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 strategies in quite a few sectors.

Table of Contents

  1. Data technological know-how in a major facts world
  2. The information technology process
  3. Machine learning
  4. Handling huge facts on a unmarried computer
  5. First steps in titanic 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 tip user

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Extra info for Introducing Data Science: Big Data, Machine Learning and More, Using Python tools

Example text

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. 4 Data exploration Data exploration is concerned with building a deeper understanding of your data. You try to understand how variables interact with each other, the distribution of the data, and whether there are outliers.

DIFFERENT LEVELS OF AGGREGATION Having different levels of aggregation is similar to having different types of measurement. An example of this would be a data set containing data per week versus one containing data per work week. This type of error is generally easy to detect, and summarizing (or the inverse, expanding) the data sets will fix it. After cleaning the data errors, you combine information from different data sources. But before we tackle this topic we’ll take a little detour and stress the importance of cleaning data as early as possible.

It is an open source implementation of the Google File System. In this book we focus on the Hadoop File System because it is the most common one in use. However, many other distributed file systems exist: Red Hat Cluster File System, Ceph File System, and Tachyon File System, to name but three. 6 Big data technologies can be classified into a few main components. 3 CHAPTER 1 Data science in a big data world Distributed programming framework Once you have the data stored on the distributed file system, you want to exploit it.

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