Dr. Iris Jiang

Assistant Professor

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Biography

Iris Jiang is a lecturer in Teaching and Leadership at Macquarie University within the School of Mathematical and Physical Sciences. She is dedicated to promoting data literacy, coding, and programming, creating transformative learning experiences that extend traditional classroom settings. Her goal is to equip students with practical skills that are relevant and applicable in real-world scenarios. Additionally, Iris actively practices computational reproducibility using tools such as R Markdown and Git. She teaches undergraduate units including Coding Communication Statistics and Applied Statistics, as well as postgraduate units in Statistical Learning and Generalized Linear Models. Her leadership role in events like ASA DataFest encourages collaborative competition where students tackle complex datasets, positioning Macquarie University as a leader among Australian institutions in hosting global data analysis challenges. Iris is also involved in team-based sustainability data visualization challenges, offering students a unique opportunity to gain practical experience in data science workflows while addressing real-world challenges such as the United Nations Sustainable Development Goals.

Research Interests

Requirements for Macquarie University

Master Program
Requirements
GPA Requirement
Required:3
IELTS
Listening
Required:6
Reading
Required:6
Writing
Required:6
Speaking
Required:6
Overall
Required:7
TOEFL
Listening
Required:20
Reading
Required:20
Writing
Required:20
Speaking
Required:20
Total
Required:94
PTE
Listening
Required:58
Reading
Required:58
Writing
Required:58
Speaking
Required:58
Overall
Required:65
Prerequisites
Bachelor degree from a recognised institution Minimum 3 years full-time equivalent work experience at a managerial/professional level
Application Checklist
  • Official Academic Transcripts
  • Curriculum Vitae (CV)
  • Statement of Motivation
  • Evidence of Work Experience
Specialization Notes

Applied to Department of Business (MBA Program).