Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda
Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Exploring where the jobs are, by industry


In the previous recipe, we saw how to visualize the top-level aggregate data on pay. The employment dataset has more granular data, divided by public/private sectors and types of jobs. The types of jobs in this data follow a hierarchical coding system called North American Industry Classification System (NIACS). In this recipe, we will consider four particular industries and look at visualizing the geographical distribution of employment in these industries, restricted to private sector jobs.

We will look at four industrial sectors in this recipe:

  • Agriculture, forestry, fishing, and hunting (NIACS 11)
  • Mining, quarrying, and oil and gas extraction (NIACS 21)
  • Finance and insurance (NIACS 52)
  • Professional and technical services (NIACS 54)

How to do it...

We need to create a subset of the employment data, including the data for industrial sectors, but restricting it to the private sector, by performing the following steps:

  1. We start by filtering the data by the...