Data Science for Urban Analytics
Learn scientific techniques for analyzing city-scale data including GPS, social media, and IoT data to enhance urban services and infrastructure.
Learn scientific techniques for analyzing city-scale data including GPS, social media, and IoT data to enhance urban services and infrastructure.
This comprehensive course explores the intersection of data science and smart city development. Students learn to analyze and interpret large-scale urban data from various sources including GPS, social media, and IoT sensors. The curriculum covers fundamental data mining techniques, Python programming, and their practical applications in urban contexts. Topics include ridesharing optimization, energy-efficient building management, and urban resilience. Through hands-on projects and case studies, participants develop skills in applying data science methods to real-world smart city challenges.
Instructors:
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What you'll learn
Develop proficiency in analyzing various types of smart city data
Master essential data mining techniques for urban analytics
Apply Python programming to solve real-world urban challenges
Implement machine learning algorithms for smart city applications
Interpret data mining results for urban policy making
Understand deep learning applications in smart city contexts
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 3 modules in this course
This comprehensive course bridges data science and urban development, teaching students to analyze city-scale data for improving urban services. The curriculum progresses from fundamental statistical methods and optimization techniques to advanced topics in machine learning and deep learning. Students learn practical applications through Python programming, covering various data mining techniques including regression analysis, classification, clustering, and neural networks. The course emphasizes hands-on experience with real urban datasets and culminates in practical case studies of smart city applications. Special focus is placed on interpreting results for urban policy-making and infrastructure management.
Introduction to Data Mining
Module 1 · 3 Weeks to complete
Data Mining Tasks
Module 2 · 8 Weeks to complete
Advanced Data Mining Techniques
Module 3 · 4 Weeks to complete
Instructors
Pioneering Scholar Advancing Transportation Systems and Urban Mobility Intelligence
Dr. Satish V. Ukkusuri serves as the Reilly Professor of Civil Engineering at Purdue University's Lyles School of Civil Engineering, where he directs the Urban Mobility Networks and Intelligence Lab, bringing extensive expertise in transportation systems and urban mobility. His academic journey, which includes a BTech from IIT Madras, MS from the University of Illinois Urbana-Champaign, and Ph.D. from The University of Texas at Austin, has led to groundbreaking research in data-driven mobility solutions, disaster management, resilience of interdependent networks, connected and autonomous traffic systems, shared mobility platforms, and smart logistics. His influential work has resulted in more than 350 peer-reviewed publications, earning him prestigious recognitions including selection as a University Faculty Scholar, Fulbright Innovation and Technology Award, and the Arab-American Frontiers Fellowship by the US National Academies of Science. As a leading authority in transportation engineering, his research spans across urban computing, disaster resilience, freight transportation, and intelligent transportation systems, while his work on understanding urban mobility patterns and developing solutions for disaster management has significantly influenced both academic research and practical applications in the field. Under his leadership, his research group continues to pioneer innovative approaches to transportation challenges, particularly in areas of cyber-transportation modeling, interdependent resilience analysis, and network science, making substantial contributions to the advancement of smart city technologies and sustainable urban mobility solutions.
Pioneering Researcher in Transportation Networks and Cybersecurity Innovation
Eunhan Ka is a Ph.D. student in the Lyles School of Civil Engineering at Purdue University, where he works in the Urban Mobility Networks and Intelligence Lab under Professor Satish V. Ukkusuri's guidance. After earning his B.S. and M.S. degrees in Civil & Environmental Engineering from Seoul National University, he has established himself as an emerging expert in transportation engineering and network modeling. His research focuses on developing pioneering frameworks for enhancing resilience in Connected and Autonomous Vehicles network traffic dynamics, with particular emphasis on cybersecurity in transportation systems. As part of Purdue's national University Transportation Center team, he contributes to critical research on transportation cyber-physical-social systems, working to identify and address emerging cybersecurity threats in smart transportation infrastructure. His scholarly work spans across network modeling, machine learning, deep learning, and transportation engineering, with notable publications on physics-informed machine learning for urban networks and network-level traffic impact assessment. Through his involvement in groundbreaking projects, including the development of generalized bathtub models for large-scale urban networks and dynamic routing games for connected vehicles, he continues to advance the field of transportation engineering while addressing critical challenges in mobility system stability and security.
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