The open position is to teach one section (12-15 students) of an elective course MSEM 678 Advanced Data Analysis with R, at the graduate level in Fall 2025. This class counts as an elective course towards completion of MSEM requirements. The course can also be counted towards the GIS certification as it has a geospatial module. 

The following course description and learning objectives can be modified depending on instructor preferences and expertise. Examples and datasets used can be changed, but the course needs to remain focused on environmental data analyses and geospatial analyses skills development. 

Course description: This course will cover statistical methods for environmental data analysis and discuss their utility as a tool for environmental decision making under conditions of uncertainty. The course builds on ENVM603 Quantitative methods and will present statistical methods that are commonly used in environmental applications. The first half of the course will cover analysis of variance and experimental design including power analysis, statistical analysis with non-normal distributions and multiple linear regression. The second half of the course will describe the basic concepts of specialized statistical analysis and will include topics like geostatistics, uncertainty quantification, time series analysis and extreme value statistics. Students will be introduced to the R/Rstudio statistical software, and will learn through hands-on practice in the classroom. Emphasis will be given on learning how to understand and interpret statistical analysis in published scientific articles. In addition to homework and in class practice, students will complete a course project where they design/analyze a study of their choice focusing on data analysis aspects and modeling methodology.  

Learning Goals and Objectives: 

1. Formulate scientific hypotheses relevant to environmental problem solving and natural resources management and apply appropriate statistical methods to test it depending on data available.
2. Understand, interpret and critically assess statistical analysis presented in published scientific articles.
3. Understand the underlying theory and applicability of statistical methods for geospatial data.
4. Gain familiarity and basic skills with state-of-the-art software and analytical techniques.

  ●    The course consists of weekly, 3-hour meetings which combine lecture and lab.

  ●    This 2-unit course runs for 8 weeks from mid-October to mid-December excluding Thanksgiving weekend.

  ●    The course can be taught in either of the two following formats:

        ○    4 in-person Saturday class meetings (9am - 12 noon, 1pm - 4pm) on alternating Saturdays OR

        ○    8 in-person weekly evening meetings (6:30-9:30PM)

  ●    Courses grades are due within 2 weeks of the last class meeting (Dec 31, 2015)

  ●    Two office hours per week are recommended during the semester.

The material for the course is provided, but can be modified to accommodate instructor preferences and expertise. The faculty member will assume responsibility and accountability for: 

  ●    Teaching to the approved syllabus and facilitating student learning by conducting lectures, encouraging student engagement and participation,  and holding office hours.

  ●    Evaluating student performance; providing frequent student feedback to enable success; alerting students with poor performance at mid-term and assigning course grades at the end of the course.

Compensation estimated at $4512 for the semester.