We are living in a data-driven world. Every sector in our global economy is using data to make better management and policy decisions. The mission of the AU School of Public Affairs' new Center for Data Science is to provide technical, managerial, and institutional resources to aid research and grant development focused on the production, analysis, and storage of 21st century data.
Across public, private, and nonprofit sectors, big data is making a big difference. Those with the right skills will lead this change. In fact, Data Scientist has ranked the number one job in America for the last three consecutive years. And by 2020, it is predicted that there will be a 28 percent increase in data science jobs.
In 2018, SPA established its Center for Data Science to focus on the theoretical and practical research aspects of computer technology, software engineering, computer architecture, artificial intelligence, simulation, modeling, and much more.
Spring 2020 Data Science Related Courses
Computer Vision (CSC 476/676): This course provides an introduction to computer vision, including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification, scene understanding, and deep learning with neural networks. Taught by Dr. Bei Xiao.
Taming Big Data (COMM 420/620): The course introduces students to R, covering basic R functions and text mining techniques including sentiment analysis and unsupervised machine learning. Students learn to scrape text from websites and social media platforms like Twitter and YouTube and analyze them using R. In addition, the course examines Big Data as a socio-technical phenomenon. Taught by Dr. Saif Shahin.
GIS Applications in Empirical Economics (ECON 696): This course leverages ArcGIS, a leading software for creation and analysis of geocoded data, to introduce the GIS tools most commonly applied in modern empirical economics. Topics include visualization of spatial data, construction and combination of variables capturing geographic proximity, climate, terrain, and other environmental characteristics for different spatial units of analysis, georeferencing, processing of raster files including satellite images and panel datasets. Taught by Dr. Boris Gershman.
Applied Natural Language Processing Using Python (STAT 696): Covers fundamental methods for analyzing textual datasets, focusing on applying classical natural language processing (NLP) methods and libraries in Python to interesting corpora. Topics include regular expressions, dictionary methods, an introduction to linguistic structure, bag-of-words methods and word/document embedding methods. Taught by Dr. Marco Enriquez from the SEC.
"Bayesian Regression for Group Testing Data"
Joshua Tebbs, Professor of Statistics, University of South Carolina November 8, 2019
Workshop on Spatial Statistics for the Social Sciences
June 14, 2019
Workshop: Text As Data
Justin Grimmer, Professor, Department of Political Science, Stanford University May 23-24, 2019
For detailed information on recent events, see the "Workshops" tab on the left navigation menu.
Learn more about Jeff Gill, Distinguished Professor of Government, Professor of Statistics, and Member of the Center for Behavioral Neuroscience at American University. Learn More