Insegnamento a.a. 2023-2024

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

• 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.

• Leverage real-world datasets and examples to apply data mining techniques
• Read and understand studies utilizing data mining techniques
• Apply different data mining techniques to research questions

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Individual assignments
• Group assignments

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.

• 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.

• Final Exam (100%)

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

Course materials posted on Black Board

• 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.