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Who / When / Where
Instructor: Zhiyu Wan
Teaching Assistant: Feiyang Zhou
Semester: Fall 2026
Location & Time:
– SIST 1A106, 15:00-16:40 on Mondays
– SIST 1D108, 10:15-11:55 on Wednesdays (Even Week)
Office Hours: Upon request, Location: BME Building, Room 228
Course Syllabus (PDF)
First Day of Class: September 14, 2026
Description
Medical Statistics and Data Analysis is a professional elective course for undergraduate students majoring in Biomedical Engineering. The course focuses on applying the principles and methods of mathematical statistics, together with statistical tools, to the analysis of biomedical big data in the field of biomedical engineering, with an emphasis on practical applications.
Through 48 hours of instruction, the course aims to enable students to: (1) understand and master fundamental statistical concepts, theories, and mathematical foundations; (2) understand the origins, development, research scope, and roles of medical statistics; (3) accurately collect, organize, analyze, and extract information and data according to specific research objectives; (4) master important methods in medical statistics and conduct appropriate statistical design and research; and (5) use R and Python programming languages to perform basic statistical analyses.
By mastering Python-based methods for processing and analyzing medical big data and gaining hands-on experience through project-based practice, students will establish a solid foundation for future research and engineering applications in the field of medical artificial intelligence.
Learning Goal
1. Knowledge Goals
1.1. Understand the development, scope of study, and role of medical statistics in biomedicine.
1.2. Master the fundamental theories and mathematical foundations of statistics.
1.3. Understand and master commonly used methods, including statistical inference, analysis of variance, correlation and regression analysis, data classification, clustering and dimensionality reduction, time-series analysis, and data visualization.
2. Skill Goals
2.1. Master experimental design methods and be able to analyze scientific questions and develop appropriate statistical study designs.
2.2. Develop the ability to collect, organize, analyze, and interpret data, as well as present analytical results effectively.
2.3. Proficiently use R and Python programming languages for statistical analysis and data processing.
2.4. Apply statistical methods to solve practical problems in biomedicine.
3. Comprehensive Competency Goals
3.1. Develop interdisciplinary research awareness and practical capabilities integrating mathematics, biology, medicine, and computer science.
3.2. Enhance teamwork, interpersonal communication, and academic communication skills.
3.3. Cultivate rigorous data-analytical thinking and scientific research competence.
Instructional Pedagogy
This course is primarily delivered through classroom lectures. Group discussions are used to deepen students’ understanding of theoretical concepts, while case studies and project-based practice strengthen their ability to apply statistical theories and techniques and solve complex data-analysis problems in real-world biomedical settings.
A combination of teaching and assessment approaches is adopted, including classroom lectures, in-class quizzes, homework assignments, case-based teaching, a final examination, and course projects. The course emphasizes heuristic and understanding-based teaching approaches aligned with international standards, with the aim of promoting active learning and stimulating students’ interest in scientific research.
Fundamental statistical knowledge, theories, concepts, algorithms, and statistical software are mainly introduced through lectures and classroom discussions. Building on theoretical instruction, real-world biomedical cases are incorporated to facilitate understanding. Course projects serve as heuristic and practice-oriented learning activities, guiding students to deepen their understanding of fundamental concepts through independent thinking, discussion, and hands-on practice.
Massive online open courses, expert lectures, and other supplementary teaching formats may also be incorporated. Spoken instruction will be delivered primarily in Chinese, while written materials will be presented primarily in English.
Prerequisites
Required: There is no specific programming language requirement, but students should be able to design and write basic computer programs.
Recommended: When appropriate, relevant methodology will be reviewed in class, but you should be comfortable learning about statistics, data structures, and algorithms. Prior experience with Python programming is preferred.
Grading
In addition to the final examination and homework assignments, this course primarily assesses students’ mastery of the course content through team-based course projects. Each team will consist of 1 to 2 students. Teams will select a topic of interest from a list prepared by the course coordinator, conduct a literature review, identify existing solutions, summarize unresolved problems, propose their own approaches, and locate relevant datasets for experimental validation.
Each team is required to produce data-driven experimental results, deliver an oral presentation and an poster presentation, and submit a final project report in the form of an English-language academic paper. With prior notification and approval from the course coordinator, students may also propose and investigate a project topic outside the provided list. The course project schedule is as follows:
| Week | Course Project Planning |
| 10 | Announce course project topics, and students begin forming teams. |
| 10 | Students complete team formation and begin selecting course project topics. |
| 12 | Each team completes project topic selection and begins preparing a preliminary project proposal. |
| 14 | Each team submits a 4-page preliminary project proposal. |
| 14 | The instructor and teaching assistants conduct a mid-term evaluation of each team’s preliminary project proposal and provide feedback and guidance to each team. Students then prepare their final course project based on the feedback. |
| 16 | Each team must record and submit an approximately 10-minute video of oral presentation for the course project in advance. All teams will present their posters, and the order of evaluation will not be announced in advance. Note: All teams must submit their presentation slides and recorded videos before the poster presentation begins. The course instructor and teaching assistants will evaluate and rank all team presentations and posters in a fair and impartial manner. |
| 17 | Students submit the final course project paper (an updated 10-page paper. Note: co-authors are listed without ranking, but each co-author’s responsibilities and contributions must be specified in the report). |
| 18 | The instructor and teaching assistants conduct a final evaluation of the course project papers. |
The grading breakdown for the course project is as follows: mid-term evaluation (10%) + oral presentation performance (20%) + poster presentation performance (30%) + course project paper (40%). The grading for the preliminary project proposal and the project paper is assisted by the teaching assistant and is finally determined by the instructor. The score for the oral presentation performance and the score for the poster presentation performance are obtained through a weighted average from the instructor (60%) and teaching assistants (40%).
The overall assessment breakdown is shown in the table below:
| 1 | Course Project | 30% |
| 2 | Homework Assignments | 30% |
| 3 | Final Exam | 30% |
| 4 | In-class quiz | 5% |
| 5 | Attendances and Classroom Performance | 5% |
| Total | 100% |
Tests and submitted assignments are primarily in English. There are 5 homework assignments, which include Q&A, calculation, and programming tasks, etc. Collaboration is not allowed. Use of generative AI tools such as ChatGPT or DeepSeek need to be disclosed and documented the versions. Each homework assignment carries equal weight. In-class quiz (closed-book) is given 1 time, in week 2, covering material in week 1. Grades and comments for the quiz and homework assignments are provided within four weeks.
Attendance is checked using QR codes and/or randomly checked by selecting students to answer questions in class; if a student is absent without prior notice, 1% is deducted from their overall grade, up to a maximum of 5%. Students who have excused absences may have no attendance points deducted as appropriate. Students who do not actively participate in class will be deducted 1% of their overall grade. Students who seriously disrupt classroom discipline will have at most 5% deducted from their final course grade.
Each student has a one-week grace period in total for all their submissions (including homework assignments, the project proposal report, the course project paper). For each additional day of delay, approximately one point (i.e., 1% of the overall grade) will be deducted from the submission. Any period of less than one day will be counted as one full day. Homework assignments may have bonus points or questions.
The final overall score will be converted from a percentage to a letter grade. The conversion relationship is shown in the table below:
| Grade | A+ | A | A- | B+ | B | B- | C+ | C | C- | F | EXC | P | NP | N |
| Grade Point | 4.0 | 4.0 | 3.7 | 3.3 | 3.0 | 2.7 | 2.3 | 2 | 1.7 | 0 | N/A | N/A | N/A | N/A |
| Reference Percentage-to-Grade Conversion Scale | 95-100 | 90-94 | 85-89 | 80-84 | 75-79 | 70-74 | 67-69 | 63-66 | 60-62 | 0-59 | As per the course syllabus | >=60 | <60 | No grade record |
It is recommended that if the class size exceeds 30 students, no more than 40% should receive an A- or above, and the failure rate should not exceed 10%.
Textbooks & Recommended Reading
(1) Textbook
Recommended Textbook 1：
| Book Name: Fundamentals of Biostatistics | Authors: Bernard Rosner | Translator: | ISBN: 9781305268920 |
| Publisher: Cengage Learning | Publication Month: 2015.08 | Version: 8 |
Recommended Textbook 2：
| Book Name: 医学统计学 | Authors: 李晓松 | Translator: | ISBN: 9787040540468 |
| Publisher: 高等教育出版社 | Publication Month: 2020.05 | * Version: 4 |
Recommended Textbook 3：
| Book Name: 医学统计学 | Authors: 颜艳、王彤 | Translator: | ISBN: 9787117303859 |
| Publisher: 人民卫生出版社 | Publication Month: 2021.01 | Version: 5 |
(2) Recommended Reading
Recommended Reading 1:
| Book Name: 医学统计学实习指导 | Author: 赵星、张菊英 | Translator: | ISBN: 9787040549386 |
| Publisher: 高等教育出版社 | Publication Month: 2020.8 | Version: 4 |
Recommended Reading 2:
| Book Name: 医学统计学习题解答 | Author: 颜艳、王彤 | Translator: | ISBN: 9787117337465 |
| Publisher: 人民卫生出版社 | Publication Month: 2022.10 | Version: 5 |
Recommended Reading 3:
| Book Name: Python医学实用统计分析 | Authors: 潘兴强、陈根浪 | Translator: | ISBN: 9787117341417 |
| Publisher: 人民卫生出版社 | Publication Month: 2023.01 | Version: 1 |
Recommended Reading 4:
| Book Name: Python医学数据分析入门 | Authors: 赵军、刘文婷 | Translator: | ISBN: 9787115575432 |
| Publisher: 人民邮电出版社 | Publication Month: 2022.01 | Version: 1 |
Recommended Reading 5:
| Book Name: Probabilistic Machine Learning: An Introduction | Authors: Kevin Patrick Murphy | Translator: | ISBN: 9780262046824/ 9780262369305 |
| Publisher: The MIT Press | Publication Month: 2022.03 | Version: 1 |
Academic Integrity
This course places a strong emphasis on academic integrity. Plagiarism, cheating, and other forms of academic misconduct are strictly prohibited.
“In learning, research, internships, practical training, and other academic activities, students shall uphold academic ethics and integrity, protect intellectual property rights, and embrace a scientific spirit characterized by innovation, truth-seeking, and pragmatism. Students shall strive to cultivate a rigorous, honest, self-disciplined, and collaborative approach to scholarship, becoming defenders of a sound academic environment, committed practitioners of rigorous scholarship, and inheritors of high standards of academic ethics.”
For further details, please refer to the ShanghaiTech University Regulations on Student Academic Integrity and Its Administration (Trial). Any additional course-specific requirements established by the instructor should be listed in detail.
