Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.

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Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.

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Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

by Alex Galea
Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

by Alex Galea

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Overview

Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.


Product Details

ISBN-13: 9781789951929
Publisher: Packt Publishing
Publication date: 10/31/2018
Sold by: Barnes & Noble
Format: eBook
Pages: 192
File size: 16 MB
Note: This product may take a few minutes to download.

About the Author

Alex Galea is a data analyst and Python expert. He has been doing data analysis professionally since graduating with an M.Sc in Physics at the University of Guelph in Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. More recently, Alex has been doing web-data analytics, where Python has continued to play a large part in his work. He frequently blogs about work and personal projects, which are generally data-centric and usually involve Python and Jupyter Notebooks.
Alex Galea has been professionally practicing data analytics since graduating with a Master’s degree in Physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.

Table of Contents

Table of Contents
  1. Jupyter Fundamentals
  2. Data Cleaning and Advanced Machine Learning
  3. Web Scraping and Interactive Visualizations
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