Teaching Philosophy
I love numbers and enjoy their superpower to answer scientific questions. I am also keen to providing guidance to students about data-related skills. Data science is a new and maturing field, with a variety of functions and applications emerging, from data engineering and data analysis to make educated decisions. Logical, scientific thinking is essential to helping students arrive at conclusions, but putting on a creative hat is equally important: I value and encourage curiosity as clues to observe new patterns. Often, alternative thinking is key to tackle a challenge. My objectives of teaching include: (1) to foster critical thinking skills; (2) to facilitate the acquisition of lifelong learning skills; (3) to help students develop evidence-based problem-solving strategies; and (4) to prepare students having effective oral and written scientific communication. These are skills that students can transfer to any career choice and help students become ready and able to utilize their knowledge in their studies and their future employment. Overall, my goal is to cultivate future data scientists who are able to combine scientific, creative and investigative thinking to extract meaning from a range of datasets, and to address the underlying challenge faced in reality.
Furthermore, my overall teaching philosophy is based on two principles: (1) active student learning strongly influences student-learning outcomes; and (2) assessment procedures strongly influence student acquisition of knowledge. Regardless of content, I believe that students should leave their courses with skills that they will use in their everyday lives. Therefore, I use a combination of traditional lecture and problem-based learning formats in my teaching. All students have an equal opportunity to contribute to discussions, activities, and evaluations.
Furthermore, my overall teaching philosophy is based on two principles: (1) active student learning strongly influences student-learning outcomes; and (2) assessment procedures strongly influence student acquisition of knowledge. Regardless of content, I believe that students should leave their courses with skills that they will use in their everyday lives. Therefore, I use a combination of traditional lecture and problem-based learning formats in my teaching. All students have an equal opportunity to contribute to discussions, activities, and evaluations.
Course
RENR480/580 Applied Statistics for the Environmental Sciences
Fall 2019, University of Alberta
Instructor: Zihaohan Sang (email: zihaohan at ualberta.ca)
Location & Time: GSB 866, Tuesday & Thursday 8:00 to 9:20am, Thursday 12:30-1:50pm
office & office hour: GSB 815; Thursday 2-5pm or make appointment
Instructor: Zihaohan Sang (email: zihaohan at ualberta.ca)
Location & Time: GSB 866, Tuesday & Thursday 8:00 to 9:20am, Thursday 12:30-1:50pm
office & office hour: GSB 815; Thursday 2-5pm or make appointment
The course focuses on problem formulation, method selection, and interpretation of statistical analysis. Covers data management and data visualization, statistical tests for parametric, non-parametric and binomial data, linear and non-linear regression approaches. Participants of the RENR 480 section will gain general statistical literacy and learn how to visualize and analyze data with open-source software packages. Participants in the RENR 580 section will also engage in problem-based learning by analyzing data from their thesis research project.
Lecture materials
Welcome to RENR 480/580
- Date: Sept 3rd, 2019 - Dec 5th, 2019
- Course syllabus:
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