Course 2024-2025 a.y.

20840 - DATA MINING FOR MARKETING, BUSINESS, AND SOCIETY

Department of Marketing

Course taught in English

Class timetable
Exam timetable
Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  SECS-P/08) - M (6 credits - II sem. - OP  |  SECS-P/08) - IM (6 credits - II sem. - OP  |  SECS-P/08) - MM (6 credits - II sem. - OP  |  SECS-P/08) - AFC (6 credits - II sem. - OP  |  SECS-P/08) - CLELI (6 credits - II sem. - OP  |  SECS-P/08) - ACME (6 credits - II sem. - OP  |  SECS-P/08) - DES-ESS (6 credits - II sem. - OP  |  SECS-P/08) - EMIT (6 credits - II sem. - OP  |  SECS-P/08) - GIO (6 credits - II sem. - OP  |  SECS-P/08) - DSBA (6 credits - II sem. - OP  |  SECS-P/08) - PPA (6 credits - II sem. - OP  |  SECS-P/08) - FIN (6 credits - II sem. - OP  |  SECS-P/08) - AI (6 credits - II sem. - OP  |  SECS-P/08)
Course Director:
KAI ZHU

Classes: 31 (II sem.)
Instructors:
Class 31: KAI ZHU


Suggested background knowledge

Knowledge in Python programming

Mission & Content Summary

MISSION

Data mining and machine learning has become one of the most in-demand new skills in business analytics. This course introduces the application of data mining for problems in marketing, business, and society. The course will teach practical data mining techniques and how they can be applied to derive insights from empirical data.

CONTENT SUMMARY

The course will overview how data mining can be applied to problems in marketing, business, and society. The topics includes:

 

  • Structured Data
    • Predictive Modeling Pipeline
    • Model Evaluation
    • Hyperparameter Tuning
    • Ensemble of Models
  • Unstructured Data
    • Working with Social Text
    • Inferring Sentiment and Affect
    • Word Embedding and Topic Modeling
    • Deep Learning for Computational Social Science

 


Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Understand the concept and intuition behind data mining methods.
  • Identify social and business problems that can be solved using data mining
  • Know how to apply data mining tools and techniques to real-world problems.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

Teaching methods

  • Practical Exercises
  • Individual works / Assignments
  • Collaborative Works / Assignments

DETAILS

For each topic in the course, we will combine lecture with hands-on exercises. Students will have opportunity to work with  data to practice in data mining skills and techniques.


Assessment methods

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

ATTENDING STUDENTS

  • Participation (30%) 
    • Engagement and In-class Exercise.
  • Assignments (40%) 
    • Multiple assignments to help students master data mining techniques.
  • Final Exam (30%) 
    • Test on both conceptual knowledge and programming skills learnt in this course.

 

Attendance will be registered at the beginning of all the sessions. To get the attending student status, students should be present in at least 75% of the lessons.


NOT ATTENDING STUDENTS

Test on both conceptual knowledge and programming skills learnt in this course.


Teaching materials


ATTENDING STUDENTS

Course materials posted on Black Board

 


NOT ATTENDING STUDENTS

  • Grokking Machine Learning, by Serrano, Luis, 2021. Publisher: Simon and Schuster

  • Bit by Bit: Social Research in the Digital Age, by Salganik, Matthew J., 2019. Publisher: Princeton University Press.

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