Data Science for Evaluators

Originally posted on aea356.org

By Nichole M. Stewart and Laura Pryor

tablesmallThe Growing Role of Data Science in Program Evaluation

Evaluators are increasingly expected to take on the role of data scientists. They are often active in the design and implementation of four related areas along every step of the evaluation cycle: data collection, management, analysis, and visualization. Collecting participant-level data and developing indicators to measure program outputs and outcomes is now only a small part of the puzzle. Evaluators are working with more complex data sources (administrative data), navigating and querying data management systems (ETO), exploring advanced analytic methods (propensity score matching), and using technology to visualize evaluation findings (R, Tableau).

But how do new evaluators build their “toolbox” of skills and improve their practice while still in school and early in their careers?

EXPLORE – Data science is storytelling! Taking the time to learn statistics, research design, and other data methods to summarize data and recognize trends often takes many years of experience.  But focusing on the basics helps improve the initial learning curve towards data proficiency.

  • Check out Jeffery Stanton’s introduction to data science and learning the basics of the open source R in a free e-book.
  • Select elective graduate courses introducing data science that expands your knowledge base beyond what your curriculum requires.
  • Visit Coursera and find free classes on Data Science, Data Analysis or Statistics
  • Search YouTube for free software training videos or subscribe to Lynda.com for a $25/month fee.
  • Pick up programming languages at Code Academy
  • Find professionals with similar interests in a LinkedIn group

PRACTICE – Graduate students and new evaluators should also be active in local or online groups and communities. Volunteer to take on a small data project for a professor or find a public data source and conduct your own analysis.

Tips and Techniques

Feel free to share your own suggestions for learning and practicing with data tools and methods in the comments.

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