课程大纲
COURSE SYLLABUS
1.
课程代码/名称
Course Code/Title
ESE5023 Computing and Programming for Environmental Research
2.
课程性质
Compulsory/Elective
Elective
3.
课程学分/学时
Course Credit/Hours
3/48
4.
授课语言
Teaching Language
English
5.
授课教师
Instructor(s)
Lei ZHU
6.
是否面向本科生开放
Open to undergraduates
or not
No
7.
先修要求
Pre-requisites
（ 如 面 向 本 科 生 开 放 ， 请 注 明 区 分 内 容 。 If the course is open to
undergraduates, please indicate the difference.）
C or C++; Applied Mathematics; Or permission of the instructors
8.
教学目标
Course Objectives
（如 面 向 本 科 生开 放 ， 请 注 明 区 分 内容 。 If the course is open to undergraduates, please indicate the
difference.）
This course will introduce students to modern computing software, programming tools, and practices that are
broadly applicable in their later research. This course will include introduction to Unix, version control and
data backup, programming in three commonly used languages (FORTRAN, R, and Python), tools for data
analysis and visualization, 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.
9.
教学方法
Teaching Methods
（如面向本科生 开放， 请注 明区分 内容。 If the course is open to undergraduates, please indicate the
difference.）
This course is a project-oriented, hands-on course, facilitating student learning through a combination of
lectures, in-class exercises, homework, final project, and one-on-one interaction during the office hours. All
topics will be taught through example data sets, demos, and research problems from Environmental Science.
10.
教学内容
Course Contents
（如面向本科生开放，请注明区分内容。 If the course is open to undergraduates, please indicate the
difference.）
Section 1
Unix operating system and Shell languages (I)
Section 2
Unix operating system and Shell languages (II)
Section 3
Version control and data backup with Git
Section 4
FORTRAN tutorial
Section 5
Intermediate FORTRAN: functions, modules, and debugging
Section 6
R tutorial
Section 7
Data analysis with R (I): common data formats; file I/O; data cleaning
Section 8
Data analysis with R (II): simple statistical analysis
Section 9
Data analysis with R (III): time series analysis
Section 10
Data analysis with R (IV): spatial data analysis
Section 11
Data visualization with R (I): basics of scientific plotting
Section 12
Data visualization with R (II): ggplot and map making
Section 13
Making websites with R Markdown
Section 14
Final presentation
11.
课程考核
Course Assessment
（
○
1
考核形式 Form of examination；
○
2
.分数构成 grading policy；
○
3
如面向本科生开放，请注明区分内容。
If the course is open to undergraduates, please indicate the difference.）
Students will be evaluated at the end of the semester based on their homework (bi-weekly, 30%), oral
presentation (30%), term paper (30%), and class participation (10%).
12.
教材及其它参考资料
Textbook and Supplementary Readings
There is no required textbook. Readings will be freely available from online resources made available through
the course website.
