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Programs and Courses

Degree Programs

These are degree programs at UD with large Data Science component to them.

Bioinformatics

Department: Interdisciplinary
Graduate Degree(s): MS in Bioinformatics & Computational Biology, PSM in Bioinformatics, PhD in Bioinformatics Data Science, and Graduate Certificates in Bioinformatics and Applied Bioinformatics (online)
Contact: Karen Hoober, Assistant Director, Graduate Education & Outreach

This program is an interdisciplinary program that offers multiple degree options. It is administered through the Department of Computer & Information Sciences and coordinated by the Center for Bioinformatics & Computational Biology (CBCB). The scientific curriculum is supported with the research strength, education resources, bioinformatics infrastructure, and Affiliated Faculty from ten participating Departments across the Colleges of Arts & Sciences, Engineering, Agriculture & Natural Resources, and Earth, Ocean & Environment, as well as the Delaware Biotechnology Institute and Data Science Institute. Whether their background is life sciences or computational/mathematical sciences, students, full-time or part-time, will gain core competency for job opportunities in the very rapidly growing field of bioinformatics.

Business Analytics

Department: Business Administration (MBA)
College: Alfred Lerner College of Business & Economics
Graduate Degree(s): MBA Major in Business Analytics

The Lerner MBA is designed to allow you to build a degree that fits your career goals. At the center of the program are the business core courses that give you a rock-solid foundation in business management knowledge and techniques across essential business functions. Then, based on your goals, you choose an elective courses option. With a major in Business Analytics students will gain the knowledge to inform business decisions by integrating data science, business strategy and management science.

Cybersecurity

Department: Electrical & Computer Engineering
Graduate Degree(s): MS in Cybersecurity and Graduate Certificate in Cybersecurity

Ensuring the security of the world’s and our own nation’s computers, systems, and networks is a key national security challenge.  Thus, establishing high-quality Cybersecurity educational programs is a top national priority as well as a regional imperative since trained Cybersecurity graduates are of critical importance to several large employer groups in this region.

The Cybersecurity master’s program is structured to enable professionals to gain advanced training in this field.  Unlike other programs that are solely focused on IT security, this program emphasizes design of secure software and systems, security analytics, and secure business systems.  It will train individuals that have a traditional background in engineering, computer science, information systems, or related fields to have strong security skills enabling them to develop new secure systems and/or software, to exploit analytics for security purposes, or to develop and manage secure business systems.  Thus graduates of this program will be skilled in the latest theories and practices required to address the most challenging cybersecurity issues facing the world today.

Data Science

Department: Interdisciplinary (Computer & Information Science, Mathematics, and Applied Economics & Statistics)
Graduate Degree(s): MS in Data Science
Contact: msds-director@udel.edu

MS in Data Science is as a professional masters degree with a flexible set of core requirements in statistics, mathematics, and computer and information sciences with a range of possible application areas. It is aimed at providing a solid background in the methods behind data science so that our graduates can go out into their fields and work well with data, and be better prepared for the next methods to come along to work with large and/or dynamic data sets.

Educational Statistics

College: College of Education and Human Development
Graduate Degree(s): Ph.D. in Educational Statistics and Research Methods
Contact: Zachary Collier (Program Coordinator)

The Ph.D. in Educational Statistics and Research Methods (ESRM) prepares students interested in education data science, research methods, statistics, causal inference, psychometrics, and evaluation to develop, critically evaluate, and properly use sophisticated quantitative and mixed methodologies to solve important problems in education.

Epidemiology

College: College of Health Sciences
Graduate Degree(s): Master of Public Health (MPH) in Epidemiology and PhD in Epidemiology

The Master of Public Health (MPH) in Epidemiology is designed to prepare students for a career in public health in either applied (e.g., local, state or federal public health agencies) or research settings. By providing students with a comprehensive foundation of population health principles, epidemiological methods and biostatistics, and study design, the MPH in Epidemiology will ensure students are proficient in the skills needed to successfully enter the public health workforce.

The PhD in Epidemiology provides students with advanced training in epidemiological methods and prepares them for careers in research, teaching, and applied public health. The program is designed to support students with some experience in public health who seek additional training related to research methods and their application to population health. Sample areas of focus include cancer, cardiovascular disease, and injury epidemiology, among others.

Financial Services Analytics

Department: Institute for Financial Services Analytics (IFSA)
College: Alfred Lerner College of Business & Economics
Graduate Degree(s): Ph.D. in Financial Services Analytics (FSAN)

Financial services analytics is the science of quantitative models and technologies designed for the financial services industry. It offers improvements in risk management, customer service, customized product offerings and business operation efficiency. The financial services analytics (FSAN) program at the University of Delaware is the first of its kind, developing fundamental FSAN theories, creating new data-driven decision-making tools and training researchers and professionals. The interdisciplinary program brings data analytics methods and non-traditional data sources to bear on issues important to the financial services industry, which differentiates it from programs in finance or financial engineering.

Geospatial Data Science

Department: Geography
College: College of Earth, Ocean, & Environment
Graduate Degree(s): PhD and MS in Geography, PhD in Climatology, and Graduate Certificate in Geographic Information Science
Contact: Dr. April Veness, Graduate Director

We have a growing faculty in Geospatial Data Science specialized in GIScience, Remote Sensing, Machine Learning, Big Data Analytics and Modeling, with applications in Land Change Science, Human Dimensions of Climate Change, Sustainability, and Climatology. The department also has strength in regional and large-scale climate modeling, weather forecasting, atmospheric observation, ecohydrology, political ecology, and critical geography. Funding may be available.

Hospitality Business Analytics

Department: Department of Hospitality Business Management and Institute for Financial Services Analytics (IFSA)
College: Alfred Lerner College of Business & Economics
Graduate Degree(s): Ph.D. in Hospitality Business Analytics

The mission of the Ph.D. in hospitality business analytics program is to provide advanced training to students in data science as it relates to the hospitality industry. The goal is to prepare students for highly demanding academic and research careers in top‐ranked institutions. Our faculty conduct in-depth research in various areas of study that apply to hospitality business analytics, such as revenue management, digital marketing, finance, customer experience management and human resources management. The program emphasizes analytics, which is possible because our faculty has access to the large amounts of data being generated by information and communication technologies in the industry.

Minerals, Materials and Society

College: College of Earth, Ocean, and Environment (CEOE)
Graduate Degree(s): Graduate Certificate in Minerals, Materials and Society (MMS)
Contact: Patricia Syvrud, MMS Program Development Manager

The Minerals, Materials and Society (MMS) program is one of the first programs of its kind in the United States to offer a for-credit graduate certificate aimed at industry, government and civil society professionals working across mineral and extractive supply chains. The program approaches the topic from an interdisciplinary and industrial ecology perspective with attention to key skills needed to evaluate the environmental and social impacts of the sector. We are committed to a science-based approach to the topic but with respect for multiple normative perspectives from industry, government and civil society.

Statistics

Department: Applied Economics and Statistics
Graduate Degree(s): MS in Statistics and Applied Statistics (online); and Statistics Certificate

The program in Statistics leads to the Master of Science degree and offers students the perspectives and skills necessary to understand and work as a statistician in various sectors of the economy (business, manufacturing, pharmaceuticals, or government sectors). Also, a strong intermediate level of training is offered so that students may continue graduate work and obtain the PhD degree. The department has ready access to various state-of-the-art computing and library resources. We provide a generous financial aid package to qualified candidates. The Statistics program also has an internship program for interested students. The intern program, a cooperative effort of the Statistics Program at the University of Delaware, trains students by a combination of formal university graduate courses and “hands on” application in an industrial setting. The objective of the internship is to introduce the student to the “art” aspects of Applied Statistics to complement the theoretical foundations learned in the classroom. Delaware’s many chemical, pharmaceutical and industrial companies provide a unique place in which to use the intern concept because of the problem-solving nature of their work, and the availability of experienced statisticians to guide intern work. The online Applied Statistics program features an applied research project.

Courses

These are Data Science related courses taught by the DSI Resident Faculty or Affiliated Faculty or members of the DSI Faculty Council.

Note that course numbers ending in 67 is reserved for new courses so many different courses from the same department will have this same course number. Please double check that the course title matches when doing course registration.

BINF667-010 – Applied Machine Learning

BINF685/CISC685 – Modeling and simulation for bioinformatics systems

BUAD621 – Decision Analytics and Visualization

CIEG642 – Advanced Data Analysis

CISC436/636 – Computational Biology and Bioinformatics

CISC481/681 – Artificial Intelligence

CISC482/682 – Introduction to Human-Computer Interaction

CISC483/683 – Introduction to Data Mining

CISC636 – Computational Biology and Bioinformatics

CISC637 – Database Systems

CISC684/BINF684 – Introduction to Machine Learning

CISC844/BMEG844 – Computational Biomedicine

CISC850 – Data Science

CISC867/ELEG867 – Seminar: Advanced Machine Learning

COMM306 – Digital Technology and Politics

CPEG 467/667 – Computational & Data-Intensive Research Platforms & Applications

CPEG657 – Search and Data Mining

EDUC867 – Seminar: Advanced Structural Equation Modeling

EDUC873 – Multilevel Models in Education

EGGG367 – Data Science I

ELEG491 – Ethics and Impacts of Engineering

ELEG815/FSAN815 – Statistical Learning

ELEG817/FSAN817 – Large scale machine learning

GEOG372 – Introduction to GIS

GEOG480/680 – Know Your Satellites

GEOG670 – Geographic Information Systems and Science

GEOL427/627 – Introduction to Geological Remote Sensing

MATH612 – Computational Method for Equation Solving and Function Minimization

MATH637 – Mathematical Techniques in Data Science

MATH667 – Topological Data Analysis

NURS844/HLTH844 – Population Healthcare Informatics

PHYS467 – Data Science for Scientists

PHYS667 – Machine Learning for Time Series Analysis

SPPA667-011 – Seminar: Urban Evidence Based Policy

STAT603 – Vector Spaces and Optimization

STAT611 – Linear Regression

STAT612 – Advanced Regression Techniques

STAT619 – Time Series Analysis

STAT621 – Survival Analysis

STAT675 – Logistic Regression

UAPP667 – Machine Learning for Public Policy

UAPP667-011 – Data Science Tools for Evidence-based Policy

Our Mission

The Institute aims to accelerate research in data science, serving as a nucleating effort to catalyze interdisciplinary research collaborations across fields impacting our society.