30603 - COMPUTATIONAL APPLICATIONS IN MARKETING
Department of Marketing
Course taught in English
Go to class group/s: 31
CLEAM (3 credits - I sem. - OP | SECS-P/08) - CLEF (3 credits - I sem. - OP | SECS-P/08) - CLEACC (3 credits - I sem. - OP | SECS-P/08) - BESS-CLES (3 credits - I sem. - OP | SECS-P/08) - WBB (3 credits - I sem. - OP | SECS-P/08) - BIEF (3 credits - I sem. - OP | SECS-P/08) - BIEM (3 credits - I sem. - OP | SECS-P/08) - BIG (3 credits - I sem. - OP | SECS-P/08) - BEMACS (3 credits - I sem. - OP | SECS-P/08) - BAI (3 credits - I sem. - OP | SECS-P/08)
Course Director:
KAI ZHU
KAI ZHU
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 marketing problems, which include
- Digital experimentation
- Causal inference with observational data
- Predictive modeling
- Natural language processing
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
- Group 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 (20%)
Engagement, Exercise
- Group Assignments (40%)
Group assignments on materials from lecture of the class. It is graded based on the performance of the solution and the quality of the report and presentation.
- Final Individual Assignment (40%)
Individual final assignment. Students need to write research proposal about their own idea based on what we learn in this class
NOT ATTENDING STUDENTS
- Final Exam (100%)
Test on concepts and programming skills
Teaching materials
ATTENDING STUDENTS
Data Analysis for Social Science: A Friendly and Practical Introduction. Llaudet, Elena and Kosuke Imai. Princeton University Press, 2022.
Optional: Quantitative social science: an introduction. Imai, Kosuke. Princeton University Press, 2018.
NOT ATTENDING STUDENTS
Data Analysis for Social Science: A Friendly and Practical Introduction. Llaudet, Elena and Kosuke Imai. Princeton University Press, 2022.
Optional: Quantitative social science: an introduction. Imai, Kosuke. Princeton University Press, 2018.
Last change 04/11/2022 15:19