Environmental Statistics and Research Methods
United Nations University (UNU-IAS)
Spring 2024
Location: 6F Lecture Room
Time: From April until July 2024
Lecturer: Jian Pu, TBA
Contact Information: (pu@unu.edu, TBA)
Office Hours: by appointment
As of 25 January 2024

Course Description
The environmental statistics and research methods is a course designed to help the students tounderstand and apply quantitative research methods in their research activities. This course isfocused on the application of relevant research methodology for the research problems thatstudents are interested in solving. Thus, presentation and explanation of instructor is combined withdiscussion of practical issues related to the application of research methods. Individual consultationof students with professors is expected to take place during the semester. Critical analysis of
relevant research articles is a part of the requirements. Considering that environmental
investigation often covers a broad range of disciplines from the social science to natural science,and the study systems/problems are complex, the course contain information about the main
statistical methods to interpret project results.
The latter half of the course will focus on learning Python for Data Analysis. This portion of thecourse assumes no prior knowledge of computer science for students and is a primer of computerscience with a particular focus on working with data in Python. The classes will require students touse their own computers in the classroom to practice programming along with the instructor.
Students will be able to gain solid foundation in Python working environment, data structures, data
wrangling, data plotting, modelling, and get exposed to popular libraries such as NumPy, pandas,
matplotlib, Patsy among others. The aim of this educational opportunity is to provide students withan entry point to the world of computing and showcase the usefulness of Python in addressing realworld data analysis challenges.

Course Objectives and Learning Goals
1. Enhance students’ understanding of the research process from idea formulation to data analysis
and interpretation;
2. Enable students to use gained knowledge to design their own research on a topic of personalinterest;
3. Improve students’ ability to critically read and understand the research literature;
4. Improve students’ confidence in applying selected research methods; and1

5. Equip students with the foundational knowledge and practical skills necessary to effectively use
Python for data analysis, including data cleaning and wrangling, visualization of data andmodelling, through hands-on exercises and real-world examples.

Requirements and Grading Policy
All students are expected to conduct active class participation and complete the analysis tasks ineach class. At the end of the semester, students are required to take a test.
••

Active class participation (30%, more than 80 % attendance is required)
Assignments (70%)
o Data analysis basics (35%)
o Data analysis in Python (35%)

Class Participation
The course requires students to attend all classes, to finish tasks in each class. At the discretion of
the instructor, frequent late arrivals or absences may result in a lower grade. Please note that thefirst session is of particular importance and cannot be missed. Materials of each class will be sharedby the instructor before the class.

Course Outline
LectureNo.
1TitleDate

Understand the purpose of data analysis;
Introduction of data analysis process inenvironmental researchApril 4 (Thu), 2024 ~
11:20-15:40

Instructors/Invited
Speakers if any
Jian Pu

2

Data analysis basics; one-way and two-way
ANOVA analysis

Jian Pu

3

Information visualization; Least Squares
Method; Outliers in environmental dataanalysis

4

Newton’s Method; Excel Solver; Indicators ofmultivariate analysis; Standardization ofvariates

5

Multivariate analysis: multiple regression
analysis, principal component analysis,
discriminant analysis (1)April 18 (Thu), 2024 ~
14:00-15:40

Jian Pu

6

Multivariate analysis: multiple regression
analysis, principal component analysis,
discriminant analysis (2)May 2 (Thu), 2024 ~
11:20-15:40

Jian Pu

7

Multivariate analysis: multiple regression
analysis, principal component analysis,
discriminant analysis (3)April 11 (Thu), 2024 ~
11:20-15:40

2

Jian Pu

Jian Pu

Jian Pu

8

Statistical analysis and applications in ESG
research (TBA)

May 9 (Thu), 2024 ~
14:00-15:40

9Python for Data Analysis: introduction,
environment set-up, and language basics

May 16 (Thu), 2024 ~
11:20-13:00

10

Python for Data Analysis: built-in data
structures, functions, and files

May 23 (Thu), 2024 ~
14:00-15:40

TBA

11

Python for Data Analysis: NumPy basics,arrays and vectorized computation
Python for Data Analysis: getting started withpandasMay 30 (Thu), 2024 ~
14:00-15:40
Jun 6 (Thu), 2024 ~
14:00-15:40

TBA

Python for Data Analysis: data loading,storage, cleaning and preparation, and datawrangling
Python for Data Analysis: plotting andvisualization

Jun 13 (Thu), 2024 ~
14:00-15:40

TBA

Jun 20 (Thu), 2024 ~
11:20-13:00

TBA

Python for Data Analysis: introduction tomodelling libraries and data analysis examples

Jun 27 (Thu), 2024 ~
11:20-13:00

TBA

1213

1415

Jian Pu/Jiaqi Yang
(Guest speaker willbe online)
TBATBA

Course Readings
1. How to design and evaluate research in education (ninth edition) by Jack Fraenkel & Norman
Wallen 2014 McGraw-Hill Companies New York, NY
2. Educational research: Planning conducting, and evaluating quantitative and qualitative
research. John W. Creswell, (sixth edition) 2014 Pearson Education, Inc., Upper Saddle River,
New Jersey.
3. Survey research methods (fifth edition) by Floyd J. Fowler, Jr. (Applied social research methods;v.1) 2014 Sage Publications, Inc., Thousand Oaks, CA
4. Moustakes, C.E. 1994 Phenomenological research methods. Sage publications, Inc. Thousand
Oaks, CA
5. Efron S.E., & Ravid R., 2020. Action research in education: a practical guide. (second edition)
The Guilford Press. New York, NY
6. Creswell J. W., & Poth C.N. 2018. Qualitative Inquiry & research design: Choosing among fiveapproaches. Sage Publications, Inc. Thousand Oaks, CA
7. C. Philip Wheater & Penny A. Cook (2000): Using Statistics to Understand the Environment,Routledge.
8. McKinney, W. (2022). Python for Data Analysis, 3rd Edition. O'Reilly Media, Inc.
https://wesmckinney.com/book/
Additional reading materials: to be determined and selected by instructors for each session.

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Important Information
Class Conduct & Etiquette
Students are expected to arrive on time and not to engage in disruptive behavior during class. This
includes, among other things, private side conversations, the use of cellphones and other electronicdevices, or the reading of newspapers. Cellphones should be switched off and stored in the bag. We
wish to create an atmosphere of open and tolerant discussion in the classroom and request students
to recognize every individual’s right to have an opinion. The lecturer and other students should betreated with dignity and respect, in discussions on contentious political issues where a diversity ofopinion is likely to arise. However, we also recognize that there are limits to tolerance and thelecturer reserves the right to request disciplinary action against any student who violates this policyor repeatedly shows disruptive behavior in class.
Computer Use in Class
Students are required to bring computers installed with Excel to the class. Python working
environment will be set-up together with instructor.
The use of computers (including tablets) in the classroom is restricted to carrying out course related
software use, taking notes, reading of the course material, or searching for course related
information on the internet. Any disruption of the class by cell phones, instant messaging programs
or other communication devices will not be tolerated. The lecturer reserves the right to revoke thispermission if a student is found using a computer for any non-course related activities.
Plagiarism & Academic Misconduct
Please be aware that the consequences of plagiarism are severe and students found guilty ofacademic misconduct will be punished in accordance with UNU’s academic honesty policies. The
lecturer reserves the right to run all assignments through an anti-plagiarism software provided by
the UNU. If evidence of academic misconduct on the assigned presentations, the mid-term exam or
the final essay should be found, the assignment will receive a failing grade. In case of repeatedviolations of academic conduct, the student may receive a failing grade for the entire course andwill be reported to the appropriate authorities for disciplinary action.

Invited Speakers/Lecturers Bio
Dr. Jiaqi Yang (Lecture 8)
Data Scientist & ESG Analyst, Sustainable Lab Inc., Japan
Jiaqi Yang holds a doctoral degree in Environmental Studies from The University of Tokyo. He isworking as data scientist in Sustainable lab Inc, a Japanese fintech start-up company specializing inESG/SDGs big data. He is specifically in charge of ESG/SDGs data research and analysis. He holds
4his PhD degree in Sustainability science from the University of Tokyo. Before joining the current
company, he also did internship at the United Nations Department of Economic and Social Affairs(UNDESA) with experience in designing evaluation on SDGs practices.

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