Coronavirus (COVID-19) updates:
Classes begin online only March 30; teleworking continues until further notice; all events cancelled.
There are courses available each semester through the University System of Maryland's Marine-Estuarine Environmental Sciences graduate program. Here is a snapshot of each course.
ENVIRONMENTAL STATISTICS I, MEES 698B
This course extends the quantitative training for students in the environmental sciences and encourages to take a statistical perspective when handling various inter-disciplinary environmental data. In this course, students will explore the basic practices of statistics and discover an extensive set of statistical tools for data analysis they need in their own research. The statistical programming language R is used in class, to complete homework sets, and to analyze data for the course projects.
ENVIRONMENTAL STATISTICS II, MEES 708M
This course extends the material of Environmental Statistics I to advanced topics of time series analysis and spatial statistics. Aiming at the broad audience of students in the environmental sciences, we try to incorporate as many modern methods of analysis as possible. After taking this course, students will be familiar with a variety of state-of-the-art approaches for qualitative analysis of time- and space-dependent data. Moreover, students will become competent users of these methods by practicing them in class and in their homework assignments using the statistical programming language R.
R PROGRAMMING, MEES 708N — SHORT COURSE
This course will engage you into programming in the world's most popular language for statistical computing — R. Nowadays, R is used in governmental organizations, in academia, and in industry (i.e., everywhere) for everything from financial forecasting to studying new drug efficiency to evaluating the impacts of global warming. We invite you to be a part of a rich and diverse R community and to acquire the computing skills necessary for your research.
Applied Bayesian Statistics, MEES 608R — SHORT COURSE
This course will introduce mixed effect modelling from a Bayesian perspective. Mixed model is an unifying framework for analysing continuous, count, presence / absence and zero inflated data from environmental applications. We will explore the selection, interpretation and reporting of Bayesian mixed modelling results. The statistical programming language R and packages R-INLA, JAGS will be used in the labs and projects. LEARN MORE