Renee M. P. Teate, "SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis" (John Wiley & Sons, 2021)


Economists and other social scientists are used to working with data that comes nicely organized into a table with a series of variable names across the top and a list of observations or datapoints down the right hand side. Data also naturally falls into this format when it comes from surveys we run. But the vast amounts of data generated by businesses and by all our online activities are usually organized in different ways. In corporate settings that first step of getting the right data and putting it into a table where it can be analyzed can be as important and challenging as the subsequent analyses. SQL (Structured Query Language) has been the standard language for accessing information in databases since the 1980s.

In this episode I interview Renee Teate, also known as “Data Science Renee” on Twitter, about her new book, SQL for Data Scientists: A Beginner’s Guide for Building Datasets for Analysis (Wiley, 2022). I learned about Renee from her popular blog and podcast, “Becoming a Data Scientist,” in which she talked about the paths she and others took to becoming a data scientist. While she was coming from more of an engineering background, many economists have been becoming data scientists from the other direction. They are building up their skills with databases and programming to complement their statistical and social science training, either because of new jobs in the tech sector or because of the new academic research possibilities this opens up. SQL is a crucial part of this toolkit, and this book is a great way to get started learning it.

In our conversation, we also discuss her current role as a lead data scientist at higher education analytics company Heliocampus, and some of her tips for aspiring data scientists as they apply for and interview for their first jobs.

For more information about the book (and an interactive SQL editor), go here

Host Peter Lorentzen is the Chair of the Economics Department at the University of San Francisco, where he created a new Master’s degree in Applied Economics designed specifically to train students in a combination of economics and data science skills that equips them to succeed in the new digital economy.

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Peter Lorentzen

Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy.

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