Beginning Fall 2021, the Business Analytics major will be available to students. Course requirements will include three core courses, three BAX electives (including one flexi-core class), one internship course, one capstone course, and 15 hours of electives.
Analytics programming will cover general principles of computer languages, and basic object-oriented programming principles. It will develop problem-solving skills to translate 'English' described business problems into programs written using the Python language. Analytics programming will build on topics covered in STA 235 and further develop techniques to explore and visualize data including classification and clustering (taught in Python/R).
Predictive analytics will cover machine learning and artificial intelligence techniques with a focus on business applications and decision-making. It will include tree-based techniques, ensemble models, artificial neural networks, deep learning, recommender systems, and evaluation and comparison of model performance. The course will also cover issues related to algorithmic decision-making, as well as algorithmic bias and fairness (taught in Python/R).
Optimization will cover optimization methods, specifically in terms of their application to decision-making. The topics will include decision making under certainty: linear, quadratic, nonlinear, and integer programming; and decision making under uncertainty: advanced simulation methods and dynamic programming (taught in Python/R).
Data Engineering and Management
The courses in Data Engineering and Management will provide skills related to understanding the source data available, including analysis of the processes generating and governing the data to identify limitations, and processing data in preparation for analysis. The course will also cover methods for obtaining data from varied sources, including database management and coding skills for obtaining data from relational or NoSQL databases; including graph databases; and APIs (application programming interfaces) for web scraping. Data processing will include the use of programming languages and data pipelines to create data sets with a desired format.