1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问，请
联系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
Environmental Data Analysis
2.
授课院系
Originating Department
School of Environmental Science and Engineering
3.
课程编号
Course Code
ESE335
4.
课程学分 Credit Value
3.0
5.
课程类别
Course Type
Major Elective Courses
6.
授课学期
Semester
Spring
7.
授课语言
Teaching Language
English
8.
授课教师、所属学系、联系方
式（如属团队授课，请列明其
他授课教师）
Instructor(s), Affiliation&
Contact
（For team teaching, please list
all instructors）
Lei ZHU
School of Environmental Science and Engineering
Taizhou Hall 429
zhul3@sustech.edu.cn
13342959636
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
To be announced
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
2
11.
授课方式
Delivery Method
讲授
Lectures
实验/实习
Lab/Practical
其它(请具体注明)
Other（Please specify）
总学时
Total
学时数
Credit Hours
48
0
48
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
None
13.
后续课程、其它学习规划
Courses for which this
course is a pre-requisite
None
14.
其它要求修读本课程的学系
Cross-listing Dept.
None
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
As an interdisciplinary, Environmental Science gains insights from various data sets of field studies, lab experiments,
remote sensing, and model simulations. Analyzing and visualizing data sets has become one of the most critical skills for
carrying out Environmental Science studies. However, senior ESE undergraduate students often find their opportunities
to access such courses confined, and specific training toward developing such skills limited.
This course will teach students how to apply suitable statistical methods and visualization tools to analyze environmental
data sets. Topics include environmental data sets characteristics, checking data sets, comparisons between two groups,
comparisons among several groups, correlation, simple linear regression, multiple linear regression, logistic regression,
time series analysis, and spatial data analysis. Students will also learn how to conduct data analysis properly with the R
language.
16.
预达学习成果 Learning Outcomes
This course facilitates student learning through lectures, exercises, labs, assignments, final project, and one-on-one
interactions. Students should be able to analyze and visualize environmental data sets using suitable statistical methods
and R tools. This course would also boost students’ programming skills, which are broadly applicable in their later study
and research.
17.
课程内容及教学日历 （如授课语言以英文为主，则课程内容介绍可以用英文；如团队教学或模块教学，教学日历须注明
主讲人）
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
Section 1 Introduction (2 credit hours).
Course logistics, topics to be covered, grading policy, R tutorial
Section 2 Review of statistic basics (4 credit hours)
Probability density function, distributions, statistical hypothesis, random draw, confidence interval, p-value, R
programming exercises
3
Section 3 Characteristics of environmental data sets (3 credit hours)
Types of environmental data sets, format of environmental data sets, normal distribution, log normal distribution, log
transformation, detection limit, missing values, R programming exercises
Section 4 Checking data sets: Quick summaries (2 credit hours)
Mean, median, quantile, standard deviation, variance, outliner, R programming exercises
Section 5 Checking data sets: Quick plots (4 credit hours)
Histogram, barplot, boxplot, scatterplot, time series plot, image plot, surface maps, R programming exercises
Section 6 Comparisons between two groups: t-tools (3 credit hours)
t distribution, assumptions of t-test, comparing means of two groups, R programming exercises
Section 7 Comparisons between two groups: Alternatives to t-tools (3 credit hours)
Rank-Sum test, permutation test, Welch t-test, sign test, signed-rank test, R programming exercises
Section 8 Comparisons among several groups (3 credit hours)
One-way ANOVA, F-test, two-way ANOVA, R programming exercises
Section 9 Linear combinations and multiple comparisons of means (3 credit hours)
Linear combinations of group means, multiple comparison procedures, R programming exercises
Section 10 Correlation and simple linear regression (3 credit hours)
Pearson’s test, Spearman’s test, Kendall’s test, simple linear regression, least squares regression estimation, R
programming exercises
Section 11 Assumptions for simple linear regression (3 credit hours)
Robustness of least squares inferences, model assessment, fit assessment, R programming exercises
Section 12 Multiple linear regression (3 credit hours)
Least squares estimates, model assessment, fit assessment, R programming exercises
Section 13 Over-fitting and variable selection (3 credit hours)
Over-fitting, AIC, BIC, backward selection, forward selection, step-wise selection, R programming exercises
Section 14 Logistic regression (3 credit hours)
Binary responses, binomial responses, Poisson responses, building logistic regression model, R programming
exercises
Section 15 Time series analysis (3 credit hours)
MA, AR, seasonal decomposition, ARIMA, forecasting, R programming
Section 16 Spatial data analysis (3 credit hours)
4
Raster and shape files, interpolation, spatial regression, R programming
Total credit hours: 48
18.
教材及其它参考资料 Textbook and Supplementary Readings
Agresti A., C. Franklin, and B. Klingenberg, Statistics: The Art and Science of Learning From Data, Pearson, 4th
Edition (January 7, 2016), 816 pages, ISBN-10: 0133860825, ISBN13: 978-0133860825.
Freedman D., R. Pisani, and R. Purves, Statistics, W. W. Norton & Company, 4th Edition (February 13, 2007), 720
pages, ISBN-10: 0393929728, ISBN-13: 978-0393929720.
Hastie, T., R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and
Prediction, Springer, 2nd Edition (January 1, 2016), 767 pages, ISBN-10: 0387848576, ISBN-13: 978-0387848570.
James G., D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: with Applications in R,
Springer, 1st ed. 2013, Corr. 7th printing 2017 Edition (June 25, 2013), 440 pages, ISBN-10: 1461471370, ISBN-13:
978-1461471370.
Ramsey F. and D. Schafer, The Statistical Sleuth: A Course in Methods of Data Analysis, Cengage Learning, 3rd
Edition (May 2, 2012), 784 pages, ISBN-10: 1133490670, ISBN-13: 978-1133490678.
Shumway R. and D. Stoffer, Time Series Analysis and Its Applications With R Examples, Springer, 4th ed. 2017
Edition (April 19, 2017), 575 pages, ISBN-10: 3319524518, ISBN-13: 978-3319524511.
课程评估 ASSESSMENT
19.
评估形式
Type of
Assessment
评估时间
Time
占考试总成绩百分比
% of final score
违纪处罚
Penalty
备注
Notes
出勤 Attendance
课堂表现
Class
Performance
10
小测验
Quiz
课程项目 Projects
平时作业
Assignments
30
期中考试
Mid-Term Test
期末考试
Final Exam
30
期末报告
Final
Presentation
30
其它（可根据需要
改写以上评估方
式）
Others (The
above may be
modified as
necessary)
5
20.
记分方式 GRADING SYSTEM
A. 十三级等级制 Letter Grading
B. 二级记分制（通过/不通过） Pass/Fail Grading
课程审批 REVIEW AND APPROVAL
21.
本课程设置已经过以下责任人/委员会审议通过
This Course has been approved by the following person or committee of authority