eBook - ePub
Beginning Data Science with Python and Jupyter
Use powerful industry-standard tools to unlock new, actionable insight from your existing data
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- 194 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Beginning Data Science with Python and Jupyter
Use powerful industry-standard tools to unlock new, actionable insight from your existing data
Book details
Book preview
Table of contents
Citations
About This Book
Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.
Key Features
- Get up and running with the Jupyter ecosystem and some example datasets
- Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests
- Discover how you can use web scraping to gather and parse your own bespoke datasets
Book Description
Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
What you will learn
- Get up and running with the Jupyter ecosystem and some example datasets
- Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests
- Plan a machine learning classification strategy and train classification, models
- Use validation curves and dimensionality reduction to tune and enhance your models
- Discover how you can use web scraping to gather and parse your own bespoke datasets
- Scrape tabular data from web pages and transform them into Pandas DataFrames
- Create interactive, web-friendly visualizations to clearly communicate your findings
Who this book is for
This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.
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Yes, you can access Beginning Data Science with Python and Jupyter by Alex Galea in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Processing. We have over one million books available in our catalogue for you to explore.
Information
Beginning Data Analysis with Python and Jupyter
Table of Contents
Beginning Data Analysis with Python and Jupyter
Why Subscribe?
PacktPub.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
What This Book Covers
What You Need for This Book
Installation and Setup
Installing Anaconda
Updating Jupyter and Installing Dependencies
Who This Book is for
Conventions
Reader Feedback
Customer Support
Downloading the Example Code
Errata
Piracy
Questions
1. Jupyter Fundamentals
Lesson Objectives
Basic Functionality and Features
Subtopic A: What is a Jupyter Notebook and Why is it Useful?
Subtopic B: Navigating the Platform
Introducing Jupyter Notebooks
Subtopic C: Jupyter Features
Explore some of Jupyter's most useful features
Converting a Jupyter Notebook to a Python Script
Subtopic D: Python Libraries
Import the external libraries and set up the plotting environment
Our First Analysis - The Boston Housing Dataset
Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame
Load the Boston housing dataset
Subtopic B: Data Exploration
Explore the Boston housing dataset
Subtopic C: Introduction to Predictive Analytics with Jupyter Notebooks
Linear models with Seaborn and scikit-learn
Activity B: Building a Third-Order Polynomial Model
Subtopic D: Using Categorical Features for Segmentation Analysis
Create categorical fields from continuous variables and make segmented visualizations
Summary
2. Data Cleaning and Advanced Machine Learning
Preparing to Train a Predictive Model
Subtopic A: Determining a Plan for Predictive Analytics
Subtopic B: Preprocessing Data for Machine Learning
Explore data preprocessing tools and methods
Activity A: Preparing to Train a Predictive Model for the Employee-Retention Problem
Training Classification Models
Subtopic A: Introduction to Classification Algorithms
Training two-feature classification models with scikit-learn
The plot_decision_regions Function
Training k-nearest neighbors for our model
Training a Random Forest
Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves
Using k-fold cross validation and validation curves in Python with scikit-learn
Subtopic C: Dimensionality Reduction Techniques
Training a predictive model for the employee retention problem
Summary
3. Web Scraping and Interactive Visualizations
Lesson Objectives
Scraping Web Page Data
Subtopic A: Introduction to HTTP Requests
Subtopic B: Making HTTP Requests in the Jupyter Notebook
Handling HTTP requests with Python in a Jupyter Notebook
Subtopic C: Parsing HTML in the Jupyter Notebook
Parsing HTML with Python in a Jupyter Notebook
Activity A: Web Scraping with Jupyter Notebooks
Interactive Visualizations
Subtopic A: Building a DataFrame to Store and Organize Data
Building and merging Pandas DataFrames
Subtopic B: Introduction to Bokeh
Introduction to interactive visualizations with Bokeh
Activity B: Exploring Data with Interactive Visualizations
Summary
Index
Beginning Data Analysis with Python and Jupyter
Copyright © 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
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First published: May 2018
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Contributors
About the author
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.
About the reviewer
Elie Kawerk likes to solve problems using the analytical skills he has accumulated over the years. He uses the data science process, including statistical methods and machine learning, to extract insights from data and get value out of it.
His formal training is in computational physics. He used to simulate atomic and molecular physi...
Table of contents
- Beginning Data Analysis with Python and Jupyter