Course Goal
The goal of this course is to introduce students to the concepts and techniques of data mining, and how they can be used to extract meaningful insights from large amounts of data.
Course Aims
By the end of this course, students should be able to:
- Understand the basic concepts and techniques of data mining
- Apply data mining algorithms to analyze and extract insights from large datasets
- Evaluate the effectiveness of different data mining techniques for different types of problems
- Use data visualization tools to present and communicate findings to stakeholders
Module 1: Introduction to Data Mining (3 lessons)
- Introduction to data mining
- Understanding the data mining process
- Data preparation and preprocessing techniques
Module 2: Data Mining Techniques (6 lessons)
- Classification algorithms
- Clustering algorithms
- Association rule mining
- Regression analysis
- Time series analysis
- Anomaly detection
Module 3: Data Mining Tools and Applications (6 lessons)
- Introduction to data mining tools (e.g., Weka, KNIME, RapidMiner)
- Applications of data mining in different domains (e.g., finance, healthcare, retail)
- Data mining in social media and web analytics
- Text mining and sentiment analysis
- Image and video analysis
Module 4: Evaluation and Visualization (3 lessons)
- Techniques for evaluating the effectiveness of data mining models
- Comparing and selecting different data mining algorithms
- Data visualization techniques for presenting findings and communicating insights
Module 5: Case Studies and Practical Applications (12 lessons)
- Real-world case studies and applications of data mining
- Group project: apply data mining techniques to a real-world problem, and present findings to the class.
Conclusion (3 lessons)
- Recap of course content
- Future developments in data mining
- Career paths in data mining and related fields.
This course will be delivered over a period of 30 lessons, with each lesson consisting of a combination of lectures, workshops, and group activities. Students will be assessed through a combination of individual assignments and a group project, which will be presented to the class. By the end of this course, students will have gained a solid foundation in data mining techniques and be equipped with the skills to analyze and extract insights from large datasets.