Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications.

Key Features

Get up and running with the Jupyter ecosystem and some example datasets

Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests

Discover how you can use web scraping to gather and parse your own bespoke datasets

Book Description

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.

What you will learn

Get up and running with the Jupyter ecosystem

Identify potential areas of investigation and perform exploratory data analysis

Plan a machine learning classification strategy and train classification models

Use validation curves and dimensionality reduction to tune and enhance your models

Scrape tabular data from web pages and transform it into Pandas DataFrames

Create interactive, web-friendly visualizations to clearly communicate your findings

Who this book is for

Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a 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 such as Pandas, Matplotlib, and Pandas providing you a useful head start.