30603 - COMPUTATIONAL APPLICATIONS IN MARKETING
Department of Marketing
Course taught in English
Go to class group/s: 31
Course Director:
KAI ZHU
KAI ZHU
Suggested background knowledge
Knowledge of Python programming
Mission & Content Summary
MISSION
Social technologies have created an explosion of data from our digital trace both online and offline. Online platforms such as Twitter, Reddit, Wikipedia, and Google as well as mass digitization of administrative and historical records are some salient examples. With these rich resources, new wave of computational techniques for collecting and analyzing data hold enormous opportunities for addressing social and business problems. In this class, we will combine insights and techniques from both data science and social science to explore how these novel data sources and computational methodologies can inform our understanding of social problems.
CONTENT SUMMARY
The course will overview real-world applications of various computational methodologies in empirical problems, which include
- Computational Basics
- Working with Social Data
- Social Influence
- Inferring Sentiment and Affect
- Language and Attitude Change
- Fake News and Misinformation
- Word Embedding Meets Social Applications
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Understand the core concepts of various computational techniques
- Identify social and business problems that can be solved using computaitonal methodologies
- Understand the suitable way to apply computational techniques in marketing problems
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Learn how to implement computational techniques in marketing applications
- Read and understand studies utilize computational techniques
- Acquire hands-on experience on computational techniques
Teaching methods
- Face-to-face lectures
- Online lectures
- Individual assignments
DETAILS
For each topic in the course, we will combine lecture with hands-on exercises. Students will have opportunity to work with real data set both in class and as group project to practice in quantitative analysis for social science.
Assessment methods
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ATTENDING STUDENTS
- Participation and Engagement (20%)
In-class participation, engagement, exercise
- Individual Assignments (30%)
About programming and computational skills
- Final Project (50%)
For attending students, we have no final exam. Instead, students need to write a research proposal about their own idea based on what we learn in this class. The research proposal is graded based on the quality of the proposal and presentation. The presentation of the research proposal is individual and public so we can assess and identify the personal contribution of the evaluated student.
Attendance will be registered at the beginning of all the sessions. In order to get the attending student status, students should be present in at least 75% of the lessons.
NOT ATTENDING STUDENTS
Teaching materials
ATTENDING STUDENTS
Class materials posted on Black Board
NOT ATTENDING STUDENTS
Imai, K. (2018). Quantitative social science: an introduction. Princeton University Press.
Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
Last change 01/08/2023 08:55