Environmental Data Analysis (ESE335)

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This course is about applying suitable statistical methods and visualization tools to analyze environmental data.

Topics include:

• 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)

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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.