This course introduces data mining techniques, real-world applications of those techniques and their challenges. Students learn popular data mining methods to gain insight from data and solve complex issues across industries. Students also acquire hands-on experience using current software to develop data mining solutions to scientific and business problems. Topics include the key tasks of data mining. Through the exploration of the concepts and techniques of data mining and practical exercises, students will develop skills that can be applied to business, science or other organizational problems. Additionally, students will use R and other open source tools to perform machine learning across a range of situations when working with structured data (including clustering, classification, decision tree and association rules).
- Document, analyze and translate data mining needs into technical designs and solutions
- Apply data mining concepts, algorithms and evaluation methods to real-world problems
- Employ data storytelling and dive into the data, find useful patterns, and articulate what patterns have been found, how they are found and why they are valuable and trustworthy
Tools & Concepts
- R and RStudio (arules, RWeka, class label, e1071, caret)
- Data preparation
- Concept description
- Association rule mining
- Classification and clustering
- Evaluation and analysis
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