An overview of data analytics: spreadsheet modeling, visualization, and supervised and unsupervised learning

Data science and analytics have emerged as thriving fields. As businesses and individuals produce massive volumes of data as a byproduct of online activity, a growing need exists for professionals trained to capitalize on the potential of big data by understanding how to use analytic techniques to generate valuable information from large collections of data. At the same time, online education is one of the fastest growing segments of higher education. The number of students working online toward Master's degree increases each year. The Association to Advance Collegiate Schools of Business (AACSB) and the joint task force of the Association for Computing Machinery (ACM) and Association for Information Systems (AIS) have called for data analytics in graduate curriculum. To meet these demands, this paper provides an overview of a skills-based online graduate course in which students learn statistical techniques for approaching big data. The hands-on curriculum focuses on spreadsheet modeling, data visualization, rudiments of data management and data analysis, and an introduction to data mining and predictive modeling, combined with state-of-the-art software, real world data sets, and the skills necessary to use the tools. This paper provides an overview of the course goals and curriculum, data sets and software tools used for visualization and analytics, and the online platform used as the content management system for course delivery.