ESE 335 Environmental Data Analysis
Monday 19:00-21:00
Wednesday (odd week only) 19:00-21:00
108, Business School
Course Website
https://zhu-group.github.io/ese335
Instructor
Lei Zhu
School of Environmental Science and Engineering
Office: 907, CoE North
Email: zhul3@sustech.edu.cn
Office Hours: By appointment
Teaching Assistant (TA)
Yali Li
School of Environmental Science and Engineering
Office: 113, Teaching Building #3
Email: 12332275@mail.sustech.edu.cn
Office Hours: Friday 19:00-20:00
Credit
The course has a credit of 3.0, with a total class hour of 48.
Course Description
As an interdisciplinary field, 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 studies.
However, SUSTech ESE undergraduate students often find their
opportunities to access such courses confined, and specific training
toward developing desired skills limited.
This course will teach students how to apply 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.
Course Objectives
This course will facilitate student learning with pre-class readings,
section examples, lectures, in-class exercises, assignments, exam, final
project, and one-on-one interactions. At the end of the course, 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, broadly applicable in their
later study and research.
Pre-requisites
Statistics or permission of the instructor.
Textbooks
[RS] 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.
[SS] Shumway R. and D. Stoffer, Time Series Analysis and Its Applications With R Examples, Springer, 4th Edition (April 19, 2017), 575 pages, ISBN-10: 3319524518, ISBN-13: 978-3319524511.
Other readings will be freely available from online resources and made available through the course website.
Course Requirements
Pre-class readings - Students are required to study the assigned readings prior to the class.
Laptop - Bring laptops to the class to learn R
programming.
In-class exercises - You will be asked to finish 2-3 in-class exercises at the end of each section.
Assignments - Assignments will be posted on this website. Due in class, no late assignments will be accepted.
Exam - The exam will be an in-class test. You will be allowed to bring a two-paged (A4) cheat sheet.
Final project - Each student will choose a question/topic of interest to study, make a poster, and present it to the whole class.
Participation - Participation in discussions is encouraged. You are also welcome to Office Hours held by the instructor and TA for specific questions.
Grading
Assignments (30%)
Exam (30%)
Final project - poster presentation (30%)
Class participation (10%)
Academic dishonesty - Just don’t. If you’re not interested in learning and doing the work, don’t do it. But, please don’t cheat or plagiarize. It’s insulting to both you and the instructor and no good comes from it.
Schedule