DACSS Technical Courses
The courses below are examples that count toward the technical requirement for the MS in Data Analytics and Computational Social Science and are open to DACSS students only.
DACSS 690E: Experiments In Politics And Power
From policy "nudges" to persuasive campaign ads to get out the vote efforts, experiments are increasingly being used to shape many aspects of political and social life. You will participate in, read about, and discuss various experiments related to politics, policy, and political behavior in the course.
DACSS 693S: Spatial Decision-Making And Support
This course is aimed at students who have a foundation in basic GIS techniques and applications and are interested in expanding their knowledge into their area of spatial decision-making and visualization of the decision maps. We will start with the linkage between GIScience, spatial analysis, and decision support. We will then discuss different decision-making techniques and highlight the important distinction between conventional MCDA methods and spatially explicitly multicriteria approaches. An overview of handling spatial uncertainty, as well as sensitivity analysis, will be discussed. The course will also introduce Python scripting for geoprocessing as a flexible approach to the development of spatial decision-making models.
In this course, students will:
- Learn the fundamentals of spatial decision-making and support
- Become familiar with using Python scripting to solve decision-making problems for spatial models
- Create decision analysis maps along with uncertainty/sensitivity maps in order to support the decision-making process
DACSS 693W: Web GIS
Students in WebGIS will explore web-based applications in geographic information science. This course will focus on hands-on practice using and building web-based mapping and analysis platforms, including Google Maps, ArcGIS Online, Leaflet, and Open Street Map. Along with a conceptual discussion of how the internet, web servers, and cloud-based GIS services function, students will create and host web services relevant to their coursework, research, or professional goals.
DACSS 695M: Machine Learning For Social Sciences
This course will provide an overview of machine learning (ML) with special attention to social and behavioral analytics applications. Machine learning combines insights from artificial intelligence, probability theory, statistical inference, and information theory to help automate tasks involving pattern recognition, prediction, and classification. "Learning" is analogous to "inference" in statistics, and the modern statistical toolkit includes various machine learning methods developed to handle large (and messy) datasets. The course focuses on statistical learning and is a good second or third course in statistical methods for graduate students in the social and behavioral sciences. We will examine key supervised and unsupervised learning techniques and reflect upon appropriate and inappropriate applications of such approaches for those seeking to understand the social world. We shall also discuss the ethical issues involved in automated analysis and computer-assisted decision-making, including how they may sometimes help overcome human biases and, in others, only reinforce these tendencies.
DACSS 695SR: Survey Research
This course will focus on advanced survey design and analysis topics. Topics covered include different approaches to sampling, how to construct and use survey weights, and tools for analyzing and enriching survey data, including approaches to conducting matching and multiple imputations, as well as the construction and analysis of panel data. The course will also focus on designing and analyzing survey experiments.
DACSS 697A: Geocomputation
Automated geography helps us to understand the complex geographic phenomena that are intractable to solve by conventional techniques. This class focuses on opportunities for taking a computational approach to the solution of complex spatial problems, often non-deterministic. Through introductory lab practices and foundational lectures, the course covers various computer-based models and techniques applicable to spatial science, including expert systems, cellular automata, agent-based modeling, genetic algorithms, visualization, and data mining. The goals of this course are to teach basic geocomputation concepts through theory and practice to enable students to better use of the vast spatial data, exploit the value of this information resources and expand the spatial information towards analysis and modeling. Students are expected to design and implement a project which will enable them to practice the skills that they acquired during the course.
DACSS 697B: Intro to GIS
This class serves as an introduction to Geographic Information Science (GIS). GIS is the science of spatial relationships, linking data to locations to explore relations between objects. Based in geographic thought and emerging from initial applications in natural resource management, GIS has evolved to be a universally applicable way of thinking and set of knowledge, skills, and practices. The goals of this course are to teach you basic GIS concepts through practice and theory, to enable you to make useful and meaningful contributions to various disciplines through spatial analysis. Throughout this course, you will be challenged to not only think spatially, but apply spatial analysis techniques within GIS.
DACSS 697D: Text as Data
With the recent explosion in digitized text availability, social scientists increasingly turn to computational tools for analyzing text as data. In this course, students will first learn how to convert text to formats suitable for analysis. The course will introduce and proceed through tutorials on various natural language processing approaches to treating text as data. This will include relatively simple dictionary approaches for measurement, supervised learning approaches for document classification, vector representations, contextualized embeddings, and more.
DACSS 697E: Political Networks Analysis
This course will introduce students to network analysis. The study of networks across the sciences has exploded recently. In this course, we will cover network scientific theory as it applies to the social sciences, network data collection and management, network visualization and description, and methods for the statistical analysis of networks. The course will extensively use real-world applications, and students will gain a thorough background in network analytic software. Most of the applications discussed will be drawn from political science, but this course will be relevant to anyone interested in network analytic research.
DACSS 697K: Python For ARC GIS
This course will cover a number of methods and applications in GIS. Basic automation methods of repetitive or complex tasks using Model Builder and Python scripting will be covered first. Then these methods will be applied to a number of common problems in Natural Resources, including home range definition, species habitat relationships, occupancy models, and movement analysis.