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WS22: Python Crash Course and Mini-Hackathon for Occupant-Centric Researchers
The ability to process larger and more complex data sets is becoming an important skill for research of indoor environments. There are ever-expanding ways we can leverage low-cost IoT sensors, occupant data collection using smartphones and smartwatches, and spatial data sources. The Python programming language, and its associated Data Science libraries such as Pandas, Scikit-Learn, and Seaborn, are now the mainstream open-source toolkit for processing, analyzing, and visualizing large research data sets.
AIMS AND SCOPE
This half-day workshop focuses on getting participants a running start in using these tools for data analysis focused on lab and field-based data from studies focused on characterizing occupant behavior, health, comfort, or safety in the built environment. The target audience members are researchers and professionals who want to start learning (or building upon existing) programming skills. No previous experience in coding is necessary to participate. Participants would need to bring a laptop computer with them and internet will be required.
Participants will be given access to online videos a month or so ahead of time to prepare for the workshop. These videos will help them understand the Python open-source toolkit beforehand so that the in-person workshop can focus on the hands-on analysis of various open data sets related to the occupant-centric research context. Participants are encouraged to watch the videos from the following free online course before attending the workshop: https://www.edx.org/course/Data-Science-for-Construction-Architecture-and-Engineering
The participants would be placed into small teams that would conduct facilitated analysis and the workshop would conclude with a short presentation from each team regarding the results of their miniature hackathon.
Introduction to Python/Pandas
Clayton Miller NUS
Divide into Groups and have a small Hackathon
Clayton Miller NUS
Topics: 3. Thermal comfort, 5c. Monitoring of indoor air quality - Analytics and sensors, 9a. Sustainable and healthy building - Energy efficiency, 11b. Building simulation and modelling - Interaction of structures and IEQ, 13b. Others - Special applications
Keywords: Python, Data Science, IoT, Occupant-Centric
Python Crash Course and Mini-Hackathon for Occupant-Centric Researchers
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