This course will prepare undergraduate and graduate students to leverage digital data and make better-informed choices by creating compelling visualizations and applying machine learning techniques. This course will take students through modules, which are aligned with the data analysis: collecting, cleansing, analyzing, visualizing (including information design, visual art, and cognitive principles), and making informed predictions and choices:

▪ Understand data: Concepts around digital data and how we can transform them into visualization
▪ Design principles for data visualization and storytelling
▪ Data wrangling: Data preparation and cleansing
▪ Data visualization tools and applications, e.g., Tableau and Gephi
▪ Explanatory analytics: Applications and model building for insight delivering with linear and logistic regression
▪ Predictive analytics: Applications and model building for prediction with regression and classification methods.

By theoretical explanations and practical examples, these lecture modules are designed to guide students through the key steps in execution of a data analysis and storytelling term project, which can contribute to a better decision-making process.

Besides the lectures, the course is also comprised of Lab assignments, office meet-ups, class activities, and a term project. The components of the term project include identifying needs, collecting, cleansing, processing, analyzing, visualizing, and communicating with digital data.