Doctoral Research.

Data analytics is defined as a scientific process that produces actionable insights. Students will be introduced to the concepts of data analysis, what the role of a data analyst will do, and the tools that are used to perform daily functions. This course will cover data analytics and data governance where students will learn about the fundamentals of data gathering, data mining, and how the decision-making process can be affected. This course also addresses the skills that are required to effectively communicate data to co-workers, leadership, and stakeholders. Excel proficiency is expected prior to enrollment in this course. Students should consider completing CMPL 402 Excel if they have not completed an Excel course in transfer.

Students will have the opportunity to explore more advanced data analytics methods such as collaborating on hypothesis testing and performing root cause analysis and practice presenting visualizations of data analysis that highlight the insights gained from analysis. The handling of imperfect data will also be covered.

A first course in predictive and prescriptive analytics. Supervised learning models including linear models and CART models. Model assessment and scoring methods, including cross-validation. Regularization and model tuning. Unsupervised learning models including k-means clustering. Project-based, with an emphasis on collaborative, experiential learning. Statistical software will be used and programming required.