MBA Specialization in Business Analytics

BUSI 651 – Machine Learning

Students explore the fundamental concepts and advanced techniques in machine learning and deep learning, focusing on their application in real-world business scenarios. Emphasis will be placed on best practices for data handling, model selection, and evaluation to ensure that learning algorithms can effectively identify patterns and make accurate predictions. Students will engage in hands-on activities to apply machine learning models to various datasets, analyze their performance, and refine their approaches to predict outcomes for future data, unlocking insights that can drive business decisions.  

Prerequisite: Completion of 15 credits, including BUSI 650.

BUSI 652 – Business Predictive Analytics

Students deepen their knowledge in predictive analytics and learn to apply it to business problems by converting them into analytical solutions, with a focus on incorporating principles of equity, diversity, inclusion, and Indigenization (EDII). The course emphasizes data understanding and preparation, with a focus on data quality and preprocessing. Students explore information-based, distance based and error-based learning methods to apply machine learning techniques effectively. Additionally, the course covers segmented regression, feature engineering, and time series analysis, ensuring data-driven insights align with strategic business objectives.  

Prerequisite: Completion of 24 credits, including BUSI 650. 

BUSI 653 – Cloud Computing Technologies

Organizations across the globe are increasingly moving to the cloud. Students will investigate cloud technology, best practices and economics as well as the rewards, environmental benefits of cloud solutions and risks of this rush to the cloud. They adapt and design sustainable data-oriented cloud computing solutions for business challenges while they focus on and integrate EDII concepts and solutions, explore the fundamentals of cloud computing, and build the foundational knowledge required for understanding and applying cloud computing from both practitioner and business perspectives. Cloud computing essentials, history, emerging trends, the business case for cloud computing, cloud service models, deployment models and the key components of data-oriented cloud architecture are covered. Students review emerging trends in the cloud such as Hybrid Multi-cloud, Containerization, Serverless, Cloud Native, DataOps and Cloud Security Basics.  

Prerequisite: Completion of 24 credits.