Course pre-requisite(s): Programming Level Beginner

Course Overview

This course provides a comprehensive introduction to data mining and neural networks, focusing on theoretical concepts and practical implementations in Python. Students will explore the principles of data mining, including classification, clustering, association rule mining, and anomaly detection, while also gaining a deep understanding of neural networks, their architectures, and their applications in real-world problems.
By the end of the course, students will be equipped with the skills to apply data mining techniques for extracting valuable insights from large datasets and use neural networks for predictive modeling and classification tasks.

Learning Outcomes

• Understand the core principles and methodologies of data mining and the basics of neural networks.
• Knowledge of data preprocessing techniques to clean and transform raw data into usable formats for analysis.
• Apply data mining algorithms for classification, regression, clustering, and association rule mining.
• Learn how neural networks are structured and trained, including advanced models like CNNs and RNNs.
• Gain practical experience building, training, and evaluating machine learning models using Python programming.
• Work on real-world projects and case studies to apply learned concepts practically.

Course Content

• Introduction to Data Mining
• Classification and Clustering Algorithms
• Neural Networks Fundamentals
• Advanced Neural Network Architectures
• Association Rule Mining and Anomaly Detection
• Combining Data Mining and Neural Networks
• Case Studies and Real-World Applications
• Final Project and Review

Instructional Method

The course will be developed through a theoretical-practical approach, which includes lectures, real-world examples, hands-on coding sessions, case studies, real-world applications, discussion sessions, self-directed learning, and final projects and presentations.

Required Course Materials

• Digital textbooks (open access)
• Python Programming Language (open source)
• Jupyter Notebook (Google Collab)
• Online available resources

Assessment

The course will incorporate a combination of assessments that measure both theoretical knowledge and hands-on technical proficiency. The assessments encourage continuous learning, practical application, and critical thinking. The detail is:

• Regular Assignments (20%)
• Participation and Engagement (10)%
• Case Study-Project (70%)