Beginning Data Science with Python and Jupyter: Use powerful industry-standard tools within Jupyter and the Python ecosystem to unlock new, actionable insights from your data

✍ Scribed by Alex Galea

• Publisher: Packt Publishing
• Year: 2018
• Tongue: English
• Leaves: 194
• Category: Library

✦ Synopsis

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.

Table of Contents

1. Jupyter Fundamentals
2. Data Cleaning and Advanced Machine Learning
3. Web Scraping and Interactive Visualizations