Conferences and Workshops

Demography Daze 2024

Demography Daze returns for 2024! Demography Daze, an annual workshop that brings together Duke and Carolina population scholars, postdocs and students to encourage idea exchange and incentivize collaborative population research will resume this year. Carolina Population Center (CPC) will host the 10th annual Demography Daze on Friday, September 6 from 1:00PM - 5:15PM. A reception will follow. Please join us!

NextGenPop @ Duke

NextGenPop is an undergraduate pipeline program in population research that aims to increase the diversity of the population field and nurture the next generation of population scientists. It is funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (R25 HD105602). NextGenPop Fellows will study population composition and change through the lens of pressing contemporary issues, including race and income inequalities, health disparities, immigration, and family change. This June, DUPRI will host 21 fellows from 19 universities across the nation for a 2-week, in-person, on-campus summer experience. The theme will be Inequality and Health.

Working with Big Data, SQL, and Cloud Computing using R

DUPRI will be hosting a virtual workshop, “Working with Big Data, SQL, and Cloud Computing using R,” on February 27. This 2-hour training provides an introduction to working with remote structured databases, performing SQL queries, and accessing and using cloud computing services like Amazon Web Services and Google Cloud Compute Engine. The training will focus specifically on the R programming language. Some prior experience using R is recommended.

Text Analysis Using R Workshop

DUPRI will be hosting a workshop, Text Analysis Using R, on December 1. This 4-hour training provides an introduction to computational text analysis using R. It covers data import and formatting, cleaning and prepping documents, data visualization, exploratory analysis, basic network analysis, and topic modeling. Some prior experience using R is recommended.