INFO 6960 - Data Science for Global Development

(CU-ITL)

Fall or Spring. 3 credits. Student option grading.

Enrollment limited to: graduate students. Only offered Fall of every other year.

A. Koenecke.

This seminar will showcase a variety of methods common to global development research across disciplines – from machine learning to statistics to economics to public health – and will instruct on both (a) reading out-of-discipline papers and (b) writing for a multidisciplinary audience. Weekly lectures will each focus on a different sustainable development goal through an academic lens, and will include guided paper discussions, student presentations, and featured guest lecturers spanning academia, non-profits, and NGOs.

Outcome 1: Solidify students’ ability to read papers and understand data science methods used to study development across fields (including computer science, economics, public health, and sociology literature).

Outcome 2: Increase students’ presentation skills of advanced computational research methods across disciplines.

Outcome 3: Demonstrate students’ written communication of data science research for interdisciplinary audiences.