Book Image

Applied Data Science with Python and Jupyter

By : Alex Galea
Book Image

Applied Data Science with Python and Jupyter

By: Alex Galea

Overview of this book

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.
Table of Contents (6 chapters)

Summary


In this chapter, 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 chapter, 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 chapter in this book.

In the next chapter, 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.