teaching

At UCSC, the overall goal of my teaching is to equip the next generation of environmental sciences students with data science skills. I teach at all levels, from large lower division introductory classes to small graduate level specialized classes. All my classes use the R programming language. Below, you fill find a list of classes I teach on a regular basis, with link to syllabus.

  • Data Analysis in the Environmental Sciences (ESCI160)

    • The main aim of this course is to introduce data analysis methods regularly encountered within environmental sciences. Students learn how to think about data, its uncertainty, how models and data are related and depend on underlying assumptions, and how to synthesize information contained in data. The class focuses on two main areas of study: 1) environmental sampling and risk analysis and 2) climate and environmental change detection. This class satisfies the Statistical Reasoning (SR) General Education requirement.

  • Applied Environmental Time Series Analysis (ESCI167/OCEA267)

    • This course introduces time-series analysis and applies it to answer questions about environmental change and variability. Students acquire the theoretical basis of time-series analysis, practice with environmental data and experience at interpreting and discussing the results and debating methodological choice. Students gain a critical understanding of the underlying assumptions and limitations of the methods discussed. This class is hands-on and utilizes a suite of observational datasets and outputs from Earth system models.

  • Fundamentals of Climate (OCEA90)

    • The climate of the Earth is undeniably changing. Through lectures, activities and readings, this class explores the very nature of the Earth’s climate, and how human activities are affecting it. The course introduces fundamental concepts such as the Earth’s energy budget, the greenhouse effect, the circulation of the atmosphere and ocean, the El-Niño Southern Oscillation and other low frequency modes of natural climate variations, and climate change detection and attribution. The analysis of climate change is inherently statistical. Therefore, in addition to learning about how the climate system works, students learn about the statistical methods that underpin much of what is known about climate change in R. This class satisfies the Statistical Reasoning (SR) General Education requirement.

  • Capstone seminar (ESCI 191)

    • This is a Capstone course, and therefore is intended to be an opportunity for you to synthesize information and knowledge that you have acquired during your college experience. The goal of this offering is to use the knowledge and skills you’ve gained during the ESCI major to communicate climate change and its effects on global health. Through weekly research and writing assignments, you will improve your ability to quantitatively assess climate change impacts on health, produce compelling graphical representations of your data and communicate your ideas.