Introducing data science: big data, machine learning, and more, using python tools,...

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Summary

Introducing Data Science

teaches you how to accomplish the fundamental tasks that occupy data
scientists. Using the Python language and common Python libraries, you'll
experience firsthand the challenges of dealing with data at scale and gain a
solid foundation in data science.

About the Technology

Many companies need developers with data science skills to work on projects
ranging from social media marketing to machine learning. Discovering what you
need to learn to begin a career as a data scientist can seem bewildering. This
book is designed to help you get started.

About the Book

Introducing Data Science

Introducing Data Science explains vital data science concepts and teaches you
how to accomplish the fundamental tasks that occupy data scientists. You’ll
explore data visualization, graph databases, the use of NoSQL, and the data
science process. You’ll use the Python language and common Python libraries as
you experience firsthand the challenges of dealing with data at scale.
Discover how Python allows you to gain insights from data sets so big that
they need to be stored on multiple machines, or from data moving so quickly
that no single machine can handle it. This book gives you hands-on experience
with the most popular Python data science libraries, Scikit-learn and
StatsModels. After reading this book, you’ll have the solid foundation you
need to start a career in data science.

What’s Inside

Handling large data

Introduction to machine learning

Using Python to work with data

Writing data science algorithms

About the Reader

This book assumes you're comfortable reading code in Python or a similar
language, such as C, Ruby, or JavaScript. No prior experience with data
science is required.

About the Authors

Davy Cielen

Arno D. B. Meysman

, and

Mohamed Ali

are the founders and managing partners of Optimately and Maiton, where they
focus on developing data science projects and solutions in various sectors.

Table of Contents

Data science in a big data world

The data science process

Machine learning

Handling large data on a single computer

First steps in big data

Join the NoSQL movement

The rise of graph databases

Text mining and text analytics

Data visualization to the end user

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