Co-op as a Research assistant in UBCO
During the Summer of 2024, I worked as a Research Assistant at UBCO, where I contributed to a wildfire risk dashboard through processing data coming from in-house weather stations within the okanagan region.
- PowerBI
- Python
- Problem-solving
My Tasks
Me and my colleagues were tasked with the development of a wildfire risk dashboard through Powerbi. The data for this dashboard was collected from in-house weather stations located throughout the Okanagan region. My primary responsibilities included cleaning and processing the raw data using Python, ensuring its accuracy and reliability for analysis. I also collaborated with the team to design and implement visualizations that effectively communicated wildfire risk levels to stakeholders.
What I Achieved
Conducted wildfire-focused data analysis by analyzing over 10,000 IoT sensor readings from 50 wildfire-prone locations using Python and SQL, improving regional fire risk mapping and contributing to a 30 percent increase in forecast accuracy. Built three Power BI dashboards adopted by multiple stakeholders, including 3 wildfire response teams, which reduced manual reporting time by approximately 40% and improved response planning efficiency. Performed stakeholder interviews and business analysis to align technical outputs with the needs of BC Wildfire Services and Kelowna fire teams; collaborated with environmental scientists to improve data accuracy and sensor reliability.
What I Learned
The ability to accurately clean and process large datasets is crucial for effective data analysis and visualization. I enhanced my Python skills, particularly in data manipulation libraries such as Pandas and NumPy. Aside from this the importance of teamwork and communication in a collaborative environment was highlighted to me. Working closely with colleagues and stakeholders helped me understand different perspectives and improved the overall quality of our work. Finding data that is more accurately representative of the real-world conditions is essential for making reliable predictions and decisions. I learned techniques for validating and cross-referencing data from multiple sources to ensure its integrity.
Challenges I Faced
The time constraint of being able to learn and build something meaningful within 4 months was a big challenge for me. To overcome this, I prioritized tasks effectively and focused on learning the most relevant skills quickly. Adding on to this, dividing the tasks among my colleagues and I in a way that played to each of our strengths was also a challenge. We held regular meetings to discuss our progress and reassign tasks as needed to ensure efficiency. Lastly, ensuring the accuracy and reliability of the data collected from various weather stations was a significant challenge