Beginning Data Science with Python and Jupyter

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

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.