20840 - DATA MINING FOR MARKETING, BUSINESS, AND SOCIETY
Course taught in English
Go to class group/s: 31
Lezioni della classe erogate in presenza
Basic knowledge in Python programming
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.
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
- 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 real data set both in class and as group project to practice in data mining for marketing
Continuous assessment | Partial exams | General exam | |
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x | |||
x |
- 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%)
Test on both conceptual knowledge and programming skills learnt in this course
- 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
- 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