UMBC Data Science
Browse by
The Data Science graduate program at UMBC prepares students to respond to the growing demand for professionals with data science knowledge, skills, and abilities. Our program brings together faculty from a wide range of fields who have a deep understanding of the real-world applications of data analytics. UMBC’s Data Science programs prepare students to excel in data science roles through hands-on experience, rigorous academics, and access to a robust network of knowledgeable industry professionals.
Recent Submissions
-
THE YIN AND YANG OF A DOCTOR OF SCIENCE IN INFORMATION SYSTEMS AND COMMUNICATIONS PROGRAM: PERSONAL REFLECTIONS AND REVERSE ENGINEERING
(International Association for Computer Information Systems, 2019)This reflective paper provides fresh accounts of the four co-authors’ personal experiences with respect to successfully completing the Doctor of Science in Information Systems and Communications program at Robert Morris ... -
Boeing 737 MAX: A case study of failure in a supply chain using system of systems framework
(International Association for Computer Information Systems, 2021)This paper uses the framework and pain points of the System of Systems (SoS) originated and prevalent in Systems Engineering, to investigate the failure of the Boeing 737 MAX. We posit that a supply chain can be investigated ... -
From Artificial to Emotional Intelligence: Integrating Five Types of Intelligence to Achieve Organizational Excellence
(ToKnowPress, 2019)Decision Support Systems have significance, as today firms turn to big data, machine learning, and artificial intelligence to guide strategy development and improve organizational performance. However, technology is not ... -
The Strengths, Weaknesses, Opportunities, and Threats Analysis of Big Data Analytics in Healthcare
(IGI Global, 2019-06)Improving the performance and reducing the cost of healthcare have been a great concern and a huge challenge for healthcare organizations and governments at every level in the US. Measures taken have included laws, ... -
INTEGRATING DATA ANALYTICS & KNOWLEDGE MANAGEMENT: A CONCEPTUAL MODEL
(International Association for Computer Information Systems, 2018)Data analytics may be heavily reliant on technology such as statistical models, machine learning algorithms, big data, and cloud computing; however, its outcome depends largely on human qualities such as experience, ... -
Doing more with less: An assessment of capacity utilisation using stochastic frontier and spectral analysis models in the case of Atlanta Hartsfield-Jackson International Airport
This study proposes a methodology to measure and compare the 9 operational outcomes of airport capacity before and after the implementation of 10 improvement programs at discrete periods at Atlanta Hartsfield/Jackson ... -
A comparative analysis of e-scooter and e-bike usage patterns: Findings from the City of Austin, TX
(Taylor & Francis, 2020-11-30)E-scooter-sharing and e-bike-sharing systems are accommodating and easing the increased traffic in dense cities and are expanding considerably. However, these new micro-mobility transportation modes raise numerous ... -
Agile Approaches for Cybersecurity Systems, IoT and Intelligent Transportation
(IEEE, 2021-12-20)To adapt to the rapidly increasing vulnerabilities in software products and cyber threats that exploit them, security professionals are actively working with software developers to produce more secure systems. In software ... -
Regional Economic Recovery and Resilience Toolkit
The COVID-19 pandemic had significant unprecedented impacts on the nation as a whole. In Virginia, the ensuing statewide shutdown in March of 2020 resulted in sharp increases in unemployment rates, decreases in spending ... -
Quality of Service Measure for Bike Sharing Systems
(IEEE, 2022-02-03)Bike sharing systems (BSSs) are becoming an important part of urban mobility in many cities given that they are sustainable and environmentally friendly. BSS operators spend great efforts to ensure bike and dock ... -
Driving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning
(Taylor & Francis, 2022-07-25)Driving behavior is considered as a unique driving habit of each driver and has a significant impact on road safety. This study proposed a novel data-driven Machine Learning framework that can classify driving behavior ... -
Network and station-level bike-sharing system prediction: a San Francisco bay area case study
(Taylor & Francis, 2021-07-08)The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System applying machine learning at two levels: network and station. Investigating BSSs at the station-level is ... -
Evaluating a Signalized Intersection Performance Using Unmanned Aerial Data
(2022-07-16)This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. MOEs are key parameters in determining the ... -
Using sentiment analysis to reinforce learning: The case of airport community engagement
(Elsevier, 2022-05-24) -
Improving messaging to airport community residents: An application of sentiment analysis to community engagement
(Henry Stewart Publications, 2021-06-01)Natural Language Processing has made significant progress over the last decades with the development of open-source software and dedicated libraries. Sentiment analysis has become a best practice in both private and public ... -
Connecting Pedestrians with Disabilities to Adaptive Signal Control for Safe Intersection Crossing and Enhanced Mobility
(National Transportation Library, 2018-08)This report summarizes work performed undercontract #DTFH6117C0001 toward the development of PedPal, a prototype mobile (smartphone) app designed to assist pedestrians with disabilities when crossing the street at signalized ... -
Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis
(IEEE, 2019-11-28)With the fast advancements of the Autonomous Vehicle (AV) industry, detection of Vulnerable Road Users (VRUs) using smartphones is critical for safety applications of Cooperative Intelligent Transportation Systems (C-ITSs). ... -
Ethics, Data Science, and Health and Human Services: Embedded Bias in Policy Approaches to Teen Pregnancy Prevention
(2020-06-07)Background: This study aims to evaluate the Chicago Teen Pregnancy Prevention Initiative delivery optimization outcomes given policy-neutral and policy-focused approaches to deliver this program to at-risk teens across the ... -
Developing a Novel Crowdsourcing Business Model for Micro-Mobility Ride-Sharing Systems: Methodology and Preliminary Results
(2020-07-30)Micro-mobility ride-sharing is an emerging technology that provides access to the transit system with minimum environmental impacts. Significant research is required to ensure that micro-mobility ride-sharing provides a ... -
Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data
(MDPI, 2020-10-25)As the Autonomous Vehicle (AV) industry is rapidly advancing, the classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The ...
RSS 1.0