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  • Book Overview & Buying Beginning Data Science with Python and Jupyter
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Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter

By : Galea
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Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter

4 (5)
By: Galea
Buy this Book

Overview of this book

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.
Table of Contents (5 chapters)
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Preface
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Preface
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What This Book Covers
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What You Need for This Book
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Who This Book is for
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Conventions
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Reader Feedback
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Customer Support
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Questions
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1
1. Jupyter Fundamentals
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1. Jupyter Fundamentals
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Lesson Objectives
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Basic Functionality and Features
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Our First Analysis - The Boston Housing Dataset
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Summary
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2. Data Cleaning and Advanced Machine Learning
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2. Data Cleaning and Advanced Machine Learning
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Preparing to Train a Predictive Model
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Training Classification Models
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Summary
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3. Web Scraping and Interactive Visualizations
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3. Web Scraping and Interactive Visualizations
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Lesson Objectives
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Scraping Web Page Data
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Interactive Visualizations
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Summary
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Index
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Index

Summary

In this lesson, you have seen the fundamentals of data analysis in Jupyter.

We began with usage instructions and features of Jupyter such as magic functions and tab completion. Then, transitioning to data-science-specific material, we introduced the most important libraries for data science with Python.

In the latter half of the lesson, we ran an exploratory analysis in a live Jupyter Notebook. Here, we used visual assists such as scatter plots, histograms, and violin plots to deepen our understanding of the data. We also performed simple predictive modeling, a topic which will be the focus of the following lesson in this book.

In the next lesson, we will discuss how to approach predictive analytics, what things to consider when preparing the data for modeling, and how to implement and compare a variety of models using Jupyter Notebooks.

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