Digital marketers today have access to a wide range of analytics and other user experience evaluation methods.
Please note that we strive to keep our electives as up-to-date as possible, but the exact courses on offer are subject to change and different electives are offered at each of our campuses.
This course focuses on solving business problems with the use of data. The objective of this course is for participants to gain an understanding of how the utilization of current computational techniques can lead to better decisions by managers and entrepreneurs. The course introduces various statistical methods with a lecture and case-based instruction. This course aims to differentiate between qualitative and quantitative analysis, to develop the ability to structure decision-making, and to explore the use of statistical and quantitative methods to better balance risks.
This course explores the concepts and techniques of data mining, a promising and flourishing frontier in database systems. This course covers data mining tasks like constructing decision trees, finding association rules, classification, and clustering. This course is designed to provide students with a broad understanding of the design and use of data mining algorithms.
We are living in an age of information overload. Large amounts of data are generated by humans, computers, and instruments, and much of the data are available on the internet. Today’s big challenge is not about generating data, but how to analyze them. While looking at numbers and texts can provide insights, the enormous data size often makes this task impractical. One solution is to use data visualization tools to convert the numbers and texts into pictures or interactive visual presentations.
This course covers the basic theories of data visualization, such as data types, chart types, visual variables, visualization techniques, structure of data visualization, navigation in data visualization, color theory, and visualization evaluation. In this course, students will discover strategies for simplifying data overload and learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis will be placed on the identification of patterns, trends and differences from data sets across categories, space, and time using tools such as Tableau and Google Charts.