Data Science Experts in Focus: An Interview with Dr. Richard (Zhen) Tang
Tell us a little about yourself and your work.
I am Zhen Tang, who also goes by Richard; I am from Huaiyuan, a beautiful one-million-population small town in eastern China. Though I learned BASIC programming on my own and wanted to be a software engineer when I was in high school, I graduated from East China University of Science and Technology with B.A. and M.S. in Business Administration. I then earned my Ph.D. in Marketing with a minor in Economics from the University of Arizona. I am now serving as an Assistant Professor of Marketing at Loyola Marymount University (LMU). At LMU, I teach marketing analytics and natural language processing and mentor students in various data science competitions.
My training on quantitative research methods consists of econometrics and machine learning (focusing on natural language processing). I am interested in applying those quantitative methods to generate constructive insights for businesses and society. Topics of my current research include quantifying business environments with geographical location information, extracting consumer insights from user-generated-content, assessing the effectiveness of AI-based service robots, and redesigning organizational structure to unleash the power of business analytics.
What inspired you to pursue marketing?
This is very interesting. As I said, I wanted to be a software engineer when I was in high school, but I was “assigned” to study marketing in the university, a much less popular area at that time, because of the extremely competitive university application environment in China. So I should say that I was not very interested in marketing at the beginning. However, marketing is a domain connecting companies, consumers, and society, critical to people’s welfare. I view myself as a considerate person — always thinking about others’ feelings — a trait that marketers need. Further, most marketing activities are digitalized, and I can apply my skills to the resultant rich data to create much more value for others.
Do you have any advice for young data scientists looking to break into the industry?
Terms like data science and data analytics are so popular that they become buzzwords. Many people talk about them, but fewer of them think critically about them, leading to confusion and frustration for many people. As practitioners in the data field, we need to be responsible — possess solid methods, practice valid data analytics, communicate clear logic and insights, and more importantly, be humble — until we understand others and their contexts, can our methods be applied to any substantive areas.
How do we evaluate data analytics findings?
For most of us, we are not the “producers” of data analytics findings; instead, we are the “consumers” of those findings. We need to be critical to those findings as data analytics can be misleading on many fronts — context, data, method, and interpretation.
I will host a seminar on this topic in the pre-conference session of IM DATA conference.
What is the best way for someone interested in NLP to get started?
Roughly speaking, NLP is a process of transforming unstructured data like texts into meaningful numbers and applying proper methods to those numbers for insights. I have complied a self-guided learning path with many resources for people who are interested in NLP and its applications in business. Here is the link.
