Ikedichi Edward Azuh

My teaching style is interactive and application-driven, ensuring that students not only grasp theoretical concepts but also learn how to apply them in real-world scenarios. I integrate hands-on coding exercises, case studies, and real datasets into lessons to make statistical concepts more tangible. Depending on the topic, I use a mix of lectures, group discussions, problem-solving sessions, and software demonstrations (R and Python).

I teach master’s-level courses such as Environmental Statistics and Research Skills. My curriculum typically covers:

- Environmental Statistics: Statistical modeling of ecological data, spatial statistics, time series analysis, and uncertainty quantification.

- Research Skills: Experimental design, data collection strategies, reproducible research, and academic writing in data-driven studies.

I ensure students engage with real-world applications by incorporating case studies, journal articles, and project-based learning.

I foster an open and engaging classroom environment where students are encouraged to ask questions and actively participate. I often use:

- Live coding demonstrations with R/Python to solve problems in real-time.
- Collaborative group projects to enhance peer learning.
- Flipped classroom techniques, where students review material beforehand and come prepared for discussions and practical exercises.

With over five years of experience as a data scientist at the University of Freiburg, I have also taken on teaching assistant roles for several courses. My role involves designing lesson plans, guiding students through statistical analyses, and mentoring them on research methodologies. I am passionate about teaching and knowledge transfer, making complex statistical concepts accessible and practical. My goal is to equip students with strong analytical skills that they can apply in academia and industry.