This repository is to share the ARCE450/550 Building Data Analytics and Visualization (BDAV) course content offered by the University of Arizona (UArizona), Department of Civil and Architectural Engineering and Mechanics (CAEM).
This BDAV course is designed for architectural and civil engineering students who are interested in data analytics, visualization, and management. The goal is for them to become more than intermediate users of the following tools.
- Excel
- Python
- MySQL
- GitHub
These tools and programming languages were chosen based on the data science tool popularity survey conducted by KDnuggets in 2019 and Dr. Jung's personal experiences.
This course only covers 'widely accessible' or 'publicly available' tools or programming languages; hence, students who have taken this course can utilize them after graduation. MATLAB, for example, is a commercialized programming language and it cannot be used without a license.
This course utilizes two datasets:
- ecobee Donate Your Data (DYD) dataset: ecobee - a Canadian smart thermostat vendor - launched the DYD program in 2015 to collaborate with researchers. Customers can 'donate' their anonymized data for the R&D purposes and Dr. Jung has published one journal article, titled Smart thermostat data-driven US residential occupancy schedules and development of a US residential occupancy schedule simulator, so far. The HUman Building Synergy (HUBS) laboratory has access to this dataset (upon agreement with ecobee) and utilizes the metdata and 20 thermostat operational data. This dataset will not be shared in this repository.
- Residential Energy Survey Consumption (RECS) data: This public dataset is adminstrated by the U.S. Energy Information Administration (EIA) and contains ~18,500 households data. More details can be found in the provided link.
Important
This repository does not share these data files due to the copyright issues.
- Python: This folder contains Python, Jupyter Notebook files, covering the basics, different useful libraries (e.g., pandas) for data analytics and visualization.
- MySQL: This folder includes multiple sql scripts that explain different functionalities in MySQL SQL language.
- After Fall 2024: This course was first taught in Fall 2024 and the scripts that students used for their hands-on activities are shared publicly in this repository.
Credit: Dr. Wooyoung Jung.