Data Science

Julia Lane, Ph.D.

Guest Speaker
Professor at the NYU Wagner Graduate School of Public Service
Professor at the NYU Center for Urban Science and Progress
NYU Provostial Fellow for Innovation Analytics

Julia Lane is a Professor at the NYU Wagner Graduate School of Public Service, at the NYU Center for Urban Science and Progress, and a NYU Provostial Fellow for Innovation Analytics. She cofounded the Coleridge Initiative, whose goal is to use data to transform the way governments access and use data for the social good through training programs, research projects and a secure data facility. The approach is attracting national attention, including the ...

What to do about Fairness in Machine Learning?

April 7, 2020
Avery Richards

How many thousands of machine learning applications have been developed and gone to market in recent years? Feeding vast amounts of data into software to make decisions for us is a social paradigm the 21st century is embracing to the fullest.

I’m a graduate student of public health, but have a long history as a social worker, student of psychology, literature and the human condition. Since early childhood, one thing I have always been is a science fiction fanatic: human, and societal relationships with technology have fascinated me to the core since before I can remember.

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Exploring Berkeley IGS Poll Data In Python

August 6, 2020
Patty Frontiera, Ph.D.

Between April 16 and 20, 2020 the Institute of Governmental Studies (IGS)(link is external), in conjunction with the California Institute of Health Equity and Action(Cal-IHEA)(link is external)...

The Importance of Design Plans for Data Science

April 20, 2021
Alex Stephenson

Since becoming a Data Fellow at the D-Lab, I have had the opportunity to assist many talented social scientists through the D-Lab’s Consulting service. A regular consulting request is to help with the research design for a new project. These requests are understandable. For empirical researchers, a high-quality research design makes or breaks a research project. In this post, I suggest a few benefits of writing a skeleton design plan before writing any code whatsoever.

One of the exciting aspects...

Handling Missing Data

May 4, 2021
Brooks Jessup, Ph.D.

I recently started working with a set of eviction data for a project on housing precarity at the Urban Displacement Project. As I began exploring the dataset, I was excited to find that it appeared to contain a wealth of historical data we could use to train a robust model for predicting eviction rates in urban neighborhoods. However, my initial excitement soon had to be scaled back when a standard check for missing data revealed that many of the observations lacked values for precisely the variable we aimed to predict. I was now faced with the problem of what to do about this...