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Whether you're a research assistant, a graduate student, or a PhD economist, you should invest early on in good coding, organizational, and workflow habits. These habits streamline the research process, and they're essential skills if you want to compete for jobs in data science.

• Develop good coding habits.

  While the coding process is being rapidly transformed by generative AI tools like ChatGPT and Cursor, a solid foundation in coding is still important for directing these tools, refining the code, and keeping project materials organized.

  • Code and Data for the Social Sciences: A Practitioner's Guide by Matt Gentzkow and Jesse Shapiro
  • Best Practices for Computer Programming in Economics by Tal Gross
  • Coding Style Guide by Michael Stepner

• Use Git to version control your code.

  Research projects can go on for years, and you should be using Git to keep track of your code as it evolves. Version control also facilitates experimentation and collaboration, and it's heavily used in industry.

  • Version Control with Git by Jon Loeliger and Matthew McCullough
  • Pro Git by Scott Chacon and Ben Straub (free e-text)
  • Git for Economists by Frank Pinter
  • Git for Teams by Emma Jane Hogbin Westby

• Become proficient in at least two programming languages.

  Stata is still widely used among economists, but researchers are increasingly switching to R or Python. In data science, Python is the leading language (followed by R), and many tech companies use SQL to query their massive data sets. You'll need to learn these languages if you want to apply for jobs in tech.

  • The Workflow of Data Analysis Using Stata by J. Scott Long
  • R for Data Science by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund
  • Python for Data Analysis by Wes McKinney

• Find the right coding environment.

  You should ideally be using an integrated development environment (IDE) with a built-in terminal and support for version control and copiloting. I used VS Code for years but have switched to Cursor, which has powerful AI tools for writing code.

• Further reading:

  • Clean Code: A Handbook of Agile Software Craftsmanship by Robert Martin
  • A Gentle Introduction to Effective Computing in Quantitative Research: What Every Research Assistant Should Know by Harry Paarsch and Konstantin Golyaev