Jingran Zhao

Dr. Jingran Zhao is an Assistant Professor of Accounting at Hong Kong Polytechnic University. She joined the School of Accounting and Finance in 2015 upon the completion of her PhD at Emory University. She received her BBA with honors and academic excellence award from Georgia College & State University in 2010.
Dr. Zhao’s research focuses on how investors use information from various sources to make investment decisions. These information sources include mandatory disclosure, voluntary disclosure, information intermediaries (e.g., analysts and news media), and social media (e.g., Twitter). Her research has been published at top accounting peer-reviewed academic journals, such as Journal of Accounting and Economics.
Dr. Zhao taught master level course on data analytics in accounting and finance, and has extensive experience conducting empirical research using various data analytics tools. She is also the co-founder of AF Tech Lab at Hong Kong Polytechnic University, where students get trained with various programming skills and learn about FinTech industry outside of the classroom. Dr. Zhao has won Award for Outstanding Teaching at School of Accounting and Finance at Hong Kong Polytechnic University. In 2020, Dr. Zhao was recognized with Award for Outstanding Achievement in teaching by Faculty of Business at Hong Kong Polytechnic University.

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Data Analytics in Accounting and Finance (edX) EdX
The Hong Kong Polytechnic University,HKPolyUx

Data Analytics in Accounting and Finance (edX)

Embark on a journey into the world of data-driven decision making with our Data Analytics in Accounting and Finance course. Designed for professionals looking to enhance their analytical skills, this course provides an interdisciplinary approach to understanding and applying data analytics within accounting and finance contexts. Whether you're an accountant, financial analyst, or aspiring professional, this course will equip you with the tools necessary to navigate the data-rich landscape of modern finance.

Self Paced
Self-Paced
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