Courses Taught by Hui Jiang
BIOSTAT600: Introduction to Biostatistics
- Graduate level
- Residential
- Fall term(s) for residential students;
- 2 credit hour(s) for residential students;
- Instructor(s): Hui Jiang (Residential);
- Prerequisites: Biostatistics students only
- Description: This course is planned as a refresher course in mathematical underpinnings that are key to statistics, like calculus, probability and linear algebra followed by discussion of basic statistical methods and concepts for all entering Biostatistics masters (and some doctoral) students. Its purpose is to prepare students for subsequent biostatistical courses.

BIOSTAT611: Grad School And Professional Success Skills
- Graduate level
- Residential
- Fall, Winter term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): Hui Jiang, Kelley Kidwell, Sebastian Zoellner, (Residential);
- Prerequisites: None
- Advisory Prerequisites: N/A
- Description: This course complements the Biostatistics curriculum in providing more tailored content to succeed as a student, on the job market, and professionally on the job. This discussion includes didactic lectures and activities led by instructors and guest speakers.
- Learning Objectives: Learn skills to survive and thrive as a Biostatistics MS student. Discuss the variety of career opportunities for biostatisticians. Prepare for internship and job opportunities. Learn skills to be a successful professional across a variety of fields.



BIOSTAT625: Computing with Big Data
- Graduate level
- Residential
- Fall term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Hui Jiang (Residential);
- Prerequisites: R module in BIOSTAT 607 or equivalent.
- Description: This course will cover techniques for computing with big data. The topics include programming, data processing, debugging, profiling and optimization, version control, software development, interfacing with databases, interfacing between programming languages, visualization, high performance and cloud computing. Hands-on experience will be emphasized in lectures, homework assignments and projects.
- Learning Objectives: (a) To master techniques for manipulating and processing big data by writing customized computer programs. (b) To understand the foundation for the computing aspects of data science. (c) To have a practical understanding of important computing issues for health big data analysis.

Department | Program | Degree | Competency | Specific course(s) that allow assessment | BIOSTAT | Health Data Science | MS | Apply basic informatics and computational techniques in the analysis of big health data, and interpret results of statistical analysis | BIOSTAT625 |
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BIOSTAT810: Approaches to the Responsible Practice of Biostatistics
- Graduate level
- Residential
- Fall term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): Hui Jiang (Residential);
- Prerequisites: None
- Description: This course will cover a series of topics that encompass Responsible Conduct of Research and Scholarship (RCRS) as defined by the National Institutes of Health (NIH), as well as focus upon the written and oral communication skills necessary for effective collaboration with public health investigators.

EPID701: Fundamentals of Biostatistics
- Graduate level
- Residential
- Summer term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Hui Jiang (Residential);
- Prerequisites: none
- Description: This course teaches the statistical methods and principles necessary for understanding and interpreting data used in public health and policy evaluation and formation. Topics include descriptive statistics, graphical data summary, sampling, statistical comparison of groups, correlation, and regression. Students will learn via lecture, group discussions, critical reading of published research, and analysis of data.
