DATA 540 Data Mining & Visualization
(XL: MATH 340) This course offers an overview of data and text mining techniques and visualizations, providing students with a solid foundation in various methodologies. Topics covered include logistic regression analyses, classical discriminant analyses, association rule mining, decision tree algorithms, support vector machines, neural networks, variable reduction techniques, cluster analysis, sentiment analysis, and web mining. This course emphasizes practical application, the course also covers essential skills for effective data visualization, including computer graphics, visual data representation, human vision models, numerical representation of knowledge, pattern analysis, and computational methods. All topics are taught using the versatile and freely available programming language R, ensuring students acquire hands-on experience with industry-standard tools and techniques.
PRQ: None