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Course 2023-2024 a.y.


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:

Classes: 31 (I sem.)
Class 31: KAI ZHU

Synchronous Blended: Lessons in synchronous mode in the classroom (for a maximum of one hour per credit in remote mode)

Suggested background knowledge

Knowledge of Python programming

Mission & Content Summary

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.


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)
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
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

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
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Individual assignment (report, exercise, presentation, project work etc.)
  • x    
  • Active class participation (virtual, attendance)
  • x    

    - 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.


    - Final Project (50%) 


    Students need to write research proposal about their own idea based on what we learn in this class. It is graded based on the performance of the solution and the quality of the report and presentation.


    - Final Exam (50%)

    Test on concepts and technical skills

    Teaching materials

    Class materials posted on Black Board


    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 08:54