Brief Introduction
Learn various scientific techniques that will allow the analysis, inference and prediction of large-scale data (e.g. GPS vehicular data, social media data, mobile phone data, individual social network data etc.) that are present in city networks.
Description
The availability of low cost and ubiquitous sensors in city infrastructure provides high granular data at unprecedented spatiotemporal scales. “Smart Cities” envision to utilize this data to provide a healthy, happy and sustainable urban ecosystem by integrating the information and communication technology (ICT), Internet of things (IoT) and citizen participation to effectively manage and utilize city infrastructure and services. “Data Science” is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge from data in various forms and provides fast and efficient understanding of the current dynamics of cities and ways to improve different services. This course will introduce scientific techniques that will allow the analysis, inference and prediction of large-scale data (e.g. GPS vehicular data, social media data, mobile phone data, individual social network data, etc.) that are present in city networks. Basics of the data science methods to analyze these datasets will be presented. The course will focus both on the methods and their application to smart-city problems. Python will be used to demonstrate the application of each method on datasets available to the instructor. Examples of problems that will be discussed include ridesharing platforms, smart and energy-efficient buildings, evacuation modeling, decision making during extreme events & urban resilience.
Knowledge
- Classify the different types of data generated by smart cities.
- Apply the basics of various data mining techniques.
- Map the data mining tool that is appropriate for various smart city applications.
- Code, apply and solve the data mining algorithms using Python.
- Interpret the results from the data mining tools and make connections to policy making as they relate to smart cities applications.
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