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Course 2023-2024 a.y.

20840 - DATA MINING FOR MARKETING, BUSINESS, AND SOCIETY

Department of Marketing

Course taught in English



Go to class group/s: 31

DES-ESS (6 credits - II sem. - OP  |  SECS-P/08)
Course Director:
KAI ZHU

Classes: 31 (II sem.)
Instructors:
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 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
  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • 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  data to practice in data mining skills and techniques.


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Individual assignment (report, exercise, presentation, project work etc.)
  • x    
  • Group assignment (report, exercise, presentation, project work etc.)
  • 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.

    Last change 15/12/2023 14:25