C. Scott Dempwolf, PhD

Assistant Research Scientist

University of Maryland College of Information

College Park, Maryland

Bio

C. Scott Dempwolf is Assistant Research Scientist in the College of Information at the University of Maryland, College Park. His current research is sponsored by the Applied Research Laboratory for Intelligence and Security (ARLIS), where he is assisting the US Navy with technical data systems. Prior research focused on data models and analytic methods for complex adaptive systems including innovation & entrepreneurial ecosystems. He teaches core iSchool courses on data science methods. Prior to joining the iSchool he was an assistant research professor in UMD’s urban planning program, where he directed an EDA-sponsored university center for 10 years, and taught economic development. Scott served as a senior advisor on ecosystems and network modeling for the Agile Strategy Lab at the University of North Alabama, and research director for The Strategic Doing Institute working on platforms supporting innovation and entrepreneurial ecosystems. Research sponsors have included the US EDA, SBA, NSF, and Kauffman Foundation, among others. Scott has authored / co-authored several highly cited papers, and numerous innovative client research reports. Prior to academia, Scott practiced economic and community development for 20 years at neighborhood, city, county, and regional levels, earning a Certified Economic Developer (CEcD) designation. He earned his PhD from University of Maryland, a master’s from Temple University, a bachelor’s from the Massachusetts Institute of Technology and an associate in arts from St. Petersburg College in Clearwater, FL.

Research Interests

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Innovation and Entrepreneurial Ecosystems

Social Network Analysis

Innovation Accelerators

Smart Cities and Urban Data Analytics

Industrial Land Use

Teaching and Courses

University of Maryland iSchool

INST123 Databases for All: An introduction to relational databases for students with no previous programming experience. Provides a means for students of diverse backgrounds to successfully learn how to store, retrieve, and maintain data in relational databases. Topics include a brief comparison of database systems with an emphasis on relational databases, fundamental relational database concepts, and data types. Includes technical approaches to accessing information stored in relational databases.
fall 2023
Syllabus

INST126 Introduction to Programming for Information Science: An introduction to computer programming for students with very limited or no previous programming experience. Topics include fundamental programming concepts such as variables, data types, assignments, arrays, conditionals, loops, functions, and I/O operations. (using Python)
fall 2023
Syllabus

INST201 Introduction to Information Science: Examining the effects of new information technologies on how we conduct business, interact with friends, and go through our daily lives. Understanding how technical and social factors have influenced the evolution of information society. Evaluating the transformative power of information in education, policy, and entertainment, and the dark side of these changes.
fall 2022, spring 2023

INST326 Object Oriented Programming for Information Science: An introduction to programming, emphasizing understanding and implementation of applications using object-oriented techniques. Topics to be covered include program design and testing as well as implementation of programs. (using Python)
spring 2024, fall 2024
Syllabus

INST755 eGovernment for Smart Cities: Federal, state, and local government entities are increasingly communicating, interacting, and providing services digitally in an online and networked environment. Concurrently, urban planners and administrators seek to leverage the potential of rapidly evolving technologies to transform service provisioning for the efficient management of assets and resources, with the goal of creating sustainable, livable, innovative, and economically vibrant cities and communities. This course will examine the intersection of these two developments and provide a framework for understanding the technical, policy, and information management issues that are emerging.
spring 2023, 2024
Syllabus

INST414 Data Science Techniques: An exploration of how to extract insights from large-scale datasets. The course will cover the complete analytical funnel from data extraction and cleaning to data analysis and insights interpretation and visualization. The data analysis component will focus on techniques in both supervised and unsupervised learning to extract information from datasets. Topics will include clustering, classification, and regression techniques. Through homework assignments, a project, exams and in-class activities, students will practice working with these techniques and tools to extract relevant information from structured and unstructured data.
fall 2022

INST314 Statistics for Information Science: Basic concepts in statistics including measure construction, data exploration, hypothesis development, hypothesis testing, pattern identification, and statistical analysis. The course also provides an overview of commonly used data manipulation and analytic tools. Through homework assignments, projects, and in-class activities, you will practice working with these techniques and tools to create information resources that can be used in individual and organizational decision-making and problem-solving.
fall 2021
Syllabus

INST630 Introduction to Programming for Information Professionals: An introduction to computer programming intended for students with no previous programming experience. Topics include fundamentals of programming and current trends in user interface implementation that are relevant to information professionals.
fall 2021
Syllabus

INFM603 Information Technology and Institutional Context: Application of communication and information technologies to support work processes, including technology-enhanced communication networks, computer-supported collaborative work, decision-support systems, interactive systems, and systems analysis. Acquisition of information systems and their integration into the organization.
fall 2021
Syllabus

University of Maryland Urban Planning Program and iSchool Joint Courses

URSP688Y / INST728G Smart Cities and Urban Data Analytics: This course frames cities as complex, adaptive, socio-technical systems where multiple academic disciplines are involved in developing a holistic, systems-level understanding of what makes them "smart". New smart-city paradigms focus on the dynamics of social interactions among people and cities. Urban data analytics are concerned with identifying, capturing, organizing, analyzing, and visualizing the streams of data that flow from these interactions using a variety of methods and tools. This course establishes a framework for understanding smart cities and for thinking critically about smart city issues in historical context from a systems perspective. From this shared foundation we will synthesize new understandings of what smart cities are along with the social and technological transformations involved as existing cities become “smart”.
spring 2022, 2021, 2020, 2019, 2018
Syllabus

University of Maryland Urban Planning Program

URSP661 City and Regional Economic Development Planning: Spatial patterns of employment and populations, and models of urban and regional growth and decline. Focus on application of economic theory and urban planning techniques to issues of local economic development and planning.
spring 2021, 2020, 2019, 2018, 2014, fall 2012; TA 2009, 2010

URSP708 Community Planning Studio: The Community Planning Studio is a "capstone" course intended to provide students with an opportunity to apply their knowledge and skills to analyze current, pressing planning issues, in a selected community and to produce a report containing recommendations for addressing those issues. In essence, students act as a consulting team for a community client.
fall 2016, 2015, 2013, 2012
Syllabus

URSP688V Special Topics: Technology-Led Economic Development: This course will introduce students to the theory, analysis and practice of technology-led economic development (TLED). TLED focuses on increasing innovation and entrepreneurship, and very often involves leveraging knowledge institutions including universities and federal research laboratories. Students will develop a theoretical framework for approaching and evaluating TLED opportunities and shortcomings; how to analyze regional capacity and opportunities for TLED; and how to translate findings into a set of realistic TLED economic development policies and projects.
spring 2011

URSP688V Special Topics: Community-Based Economic Development: This course on Community-Based Economic Development will examine economic development theories, policies and practices from the local perspective, as it is often viewed by community development corporations (CDC’s) or other community based organizations (CBO’s).
spring 2013
Syllabus

Carroll Community College

MATH115 Introduction to Statistics: Introduction to Statistical Methods is an introductory college level statistics course that provides a basic understanding of data analysis. Topics covered include measures of central tendency and variation, probability, discrete and continuous distributions, estimation and testing of population parameters, and linear correlation and regression. Statistical literacy will be emphasized throughout the course. Students in the areas of education, nursing, business, and the arts and sciences will find that this course enhances and clarifies use and application of statistics in their specializations.
fall 2012