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.
  • Syllabus for BIOSTAT600
JiangHui
Hui Jiang

BIOSTAT611: Grad School And Professional Success Skills

  • Graduate level
  • Residential
  • Fall term(s) for residential students;
  • 1 credit hour(s) for residential students;
  • Instructor(s): Kelley Kidwell, Hui Jiang, (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.
KidwellKelley
Kelley Kidwell
JiangHui
Hui Jiang

BIOSTAT612: Grad School And Professional Success Skills

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 1 credit hour(s) for residential students;
  • Instructor(s): Hui Jiang (Residential);
  • Prerequisites: None
  • 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: Continuation of Biostat 611: 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.
JiangHui
Hui Jiang

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.
  • Syllabus for BIOSTAT625
JiangHui
Hui Jiang
Concentration Competencies that BIOSTAT625 Allows Assessment On
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

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.
JiangHui
Hui Jiang