Analyze a Dataset from a Critical Gender Perspective

In the field of data science, the issue of data equity is an ongoing movement that aims to promote responsible data work. The current problem with major data practices is not that the data we have is insufficient, but that it doesn’t not accurately reflect the realities of women and non-binary people. For example, the gender binary (male/female) classification system in data collection will result in omission errors that make certain people’s experiences invisible. In this Build Project, you'll wear the hat of a Data Analyst and audit data cycle practices with a gender lens. Based on it, you'll produce a data visualization project that incorporates standard visualization frameworks and gender data best practices. Under the supervision of an experienced Build Fellow, you'll uncover the flaws in current research methodologies that have led to inaccurate and incomplete data on the status of women and non-binary persons. You'll explore marginal changes that can be made to the way that we collect, analyze, and visualize gender data. You’ll become familiar with data visualization frameworks such as Tufte’s rules, Junk Chart’s trifecta, and Schwabish’s core principles, as well as such ubiquitous industry tools as Excel, R, or Tableau.