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This course is designed to equip students with the theoretical and practical tools needed to build effective and engaging data visualizations and demonstrate competence in designing and developing visual stories with data. Students will learn visual representation methods and techniques that improve their understanding of complex data and models, with emphasis placed on the identification of patterns, trends, and differences from data sets across categories, space, and time using R, PowerBI, and Tableau. NOTE: It cannot be taken as credit towards M.S. in Applied Statistics or the Ph.D. degree or the Graduate Certificate in Statistics, but may be taken as credit for the Big Data Statistical Analysis Graduate Certificate. This course is also part of the M.S. degree program in Data Science. Prereq: STAT 711. Dual-listing: STAT 714.

This course is designed to equip students with the theoretical and practical tools needed to build effective and engaging data visualizations and demonstrate competence in designing and developing visual stories with data. Students will learn visual representation methods and techniques that improve their understanding of complex data and models, with emphasis placed on the identification of patterns, trends, and differences from data sets across categories, space, and time using R, PowerBI, and Tableau. NOTE: It cannot be taken as credit towards M.S. in Applied Statistics or the Ph.D. degree or the Graduate Certificate in Statistics, but may be taken as credit for the Big Data Statistical Analysis Graduate Certificate. This course is also part of the M.S. degree program in Data Science. Prereq: STAT 711. Dual-listing: DATA 714.