Environmental Data Analysis (ESE335)
This course is about applying suitable statistical methods and visualization tools to analyze environmental data.
- basics of statistics,
- features of environmental data,
- checking data sets,
- comparisons between two groups,
- comparisons among several groups,
- correlation tests,
- simple linear regression,
- multiple linear regression,
- logistic regression,
- and time series analysis.
Students will also learn how to conduct data analysis and visualization properly using the R language.
Computing and Programming for Environmental Research (ESE5023)
This course introduces students to modern computing software, programming tools, and practices that are broadly applicable in their later research.
This course will include:
- an introduction to Linux,
- version control and data backup with Git,
- programming in two commonly used languages (Python and FORTRAN),
- data analysis and visualization tools,
- and high-performance computing exercises on cluster computers.
This course will boost students’ programming and computing skills, which are in high demand in the era of Big Data.