Insegnamento a.a. 2022-2023

20840 - DATA MINING FOR MARKETING, BUSINESS, AND SOCIETY

Department of Marketing

Course taught in English
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)
Course Director:
KAI ZHU

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


Suggested background knowledge

Basic 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 include:

  • Introduction to Data Mining
  • Python Ecosystem for Data Science
  • Predictive Modeling
  • Model evaluation
  • Decision Tress and Random Forest
  • Logistic Regression
  • Support Vector Machine
  • Clustering Algorithms
  • Neural Networks
  • Transformers
  • Representation Learning

 


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 real data set both in class and as group project to practice in data mining for marketing


Assessment methods

  Continuous assessment Partial exams General exam
  • Individual assignment (report, exercise, presentation, project work etc.)
 x    
  • Group assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING STUDENTS

  • Participation (15%) 

Engagement and In-class Exercise

  • Assignments and Projects (25%) 

Multiple assignments to help students master the state-of-art data mining techniques.

  • Final Exam (60%) 

 


NOT ATTENDING STUDENTS

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


Teaching materials


ATTENDING STUDENTS

  • Grokking Machine Learning, by Serrano, Luis, 2021. Publisher: Simon and Schuster.
  • VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. Publisher: O'Reilly Media

 


NOT ATTENDING STUDENTS

  • Grokking Machine Learning, by Serrano, Luis, 2021. Publisher: Simon and Schuster.
  • VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. Publisher: O'Reilly Media
Last change 03/02/2023 16:07