Teaching Statement
In the rapidly evolving landscape of data science, I firmly believe that fostering data literacy is not merely a classroom objective but a vital skill for civic participation in today's world. With decisions increasingly driven by data, it is imperative for students to comprehend fundamental concepts as well as develop critical inquiry skills regarding datasets and the potential manipulation or persuasion embedded within them.
My teaching philosophy is rooted in a holistic approach, combining foundational principles with practical applications using real-world datasets. I structure my courses to provide a comprehensive understanding of data science, emphasizing the importance of critical thinking in the analysis and visualization of data. This approach allows students not only to grasp theoretical concepts but also to engage in meaningful discussions about the ethical implications and societal impact of data-driven decision-making.
Utilizing a diverse set of teaching methods, including slides, videos, games, and interactive techniques, I strive to create a dynamic and engaging learning environment. Projects assigned go beyond the curriculum, incorporating fun elements to maintain student motivation. As a researcher, I have extended this commitment to innovative teaching by hosting data storytelling workshops focused on air quality and delivering guest lectures on the topic. Data storytelling, in my view, serves as a powerful tool to combine multiple sources of data, supporting narratives that resonate with diverse audiences, and incorporating their lived experiences.
I actively work to create a learning environment that addresses the over-representation of certain groups. Post-COVID, I understand the diverse needs, responsibilities, and priorities of students. To accommodate these factors, I ensure a reasonable class-load, balancing projects, quizzes, and assignments. Drawing from my research background in citizen science, I actively promote the involvement of non-scientists in the process of collecting, analyzing, and disseminating data, amplifying under-represented voices in the field.
To enhance student engagement and relevance, I advocate for the use of local, open data with immediate implications for students. At the beginning of the semester, I seek input from students about their preferences, incorporating elements they have enjoyed from other classes. Assessment methods include assignments, quizzes, and active class participation, fostering an environment where students feel comfortable contributing to discussions.
Flexibility is a cornerstone of my teaching approach. I allow for extended assignment deadlines, understanding the diverse challenges students may face. I am accessible during office hours, after class, or by appointment, encouraging students to discuss the state of the field and seek career advice.
Incorporating the latest tools and technologies in data science, I compare and contrast online tools, showcasing diverse approaches to data analysis and visualization. As an engaged researcher, I stay current by attending conferences, ensuring that my teaching reflects the latest tools and practices used by professionals in the field.
To summarize, my classes are designed to be interactive and engaging, combining fun with learning. I maintain a professional yet approachable character in the classroom, fostering an environment that encourages curiosity, critical thinking, and a passion for lifelong learning in the field of data science.
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