Practical Data Science: Introduction

Practical Data Science: Introduction

Content Summary

This chapter introduces the term ‘data science’, describing the over-arching methodologies that data scientists follow, while illustrating these methods through a simple, non-trivial example on the fairness of public salaries.


The primary dataset of this chapter is the San Diego Employee Salary dataset from Transparent California. This dataset includes a complete record of city employees, their job titles, and a detailed breakdown of their salaries in 2017. Other years are available at the source.

Remark on privacy and ethics

This dataset contains personal information on real people. On the one hand, city employees are considered ‘public figures’ and the public has a right to know how tax-dollars are being spent. On the other hand, many of these individual employees have typical, modest jobs outside of the public spotlight, and should reasonably be able to expect privacy and the responsible use of their data.

Working this this dataset, one should exercise caution to:

  • Understand the data only to answer the overarching questions that require the dataset’s use. Do not needlessly ‘poke around’ the data; keep the investigation as ‘anonymous as possible’.

  • Proactively strip identifying information from the dataset whever it’s not needed for analysis. In this case, only the first names of employees are kept.

  • Do not propogate peoples information across the internet (e.g. on a blog post, or in the versioned project on GitHub). Just because data is public record, doesn’t mean it should be the first result on a Google search for that individual!