BSAN - Business Analytics
Courses numbered 500 to 799 = undergraduate/graduate. (Individual courses may be limited to undergraduate students only.) Courses numbered 800 to 999 = graduate.
BSAN 675. Analytics Decision Modeling with Spreadsheets (3).
Cross-listed as FIN 675. Introduces key principles of business analytics modeling: descriptive, predictive and prescriptive. Models covered in each area may differ from semester to semester. Students learn how to make decisions not based on intuition or “gut feel,” but on models and data. Course adopts a practical approach to the modeling of a wide variety of business problems in various functional areas. Models are built in Excel and add-ins to Excel, allowing students to gain advanced Excel skills, which will benefit them in their careers. Prerequisite(s): DS 350 and FIN 340 each with a grade of C or better; BADM 162, ECON 231, and ECON 232 or equivalents.
BSAN 710. Python Programming for Business (3).
Cross-listed as ECON 710. Provides students with an understanding of the fundamentals of Python programming to prepare them for the growing demand for these skills in modern business. This course uses Python Notebooks to introduce students to important Python packages essential for data analysis, such as Numpy, Pandas, Matplotlib, Scikit-learn, etc. Students learn how to program in Python; perform scientific computations; prepare, manipulate, transform and clean data; create descriptive statistics; visualize different types of data; and use the data to create analytical models. Upon successful completion of this course, students should be skillful with python programming for analytics with a solid foundation for further study in data science and a competitive edge in the contemporary workplace. Prerequisite(s): ECON 231 and either an ECON 300-level class or MIS 310; or graduate status.
BSAN 734. Introduction to Data Mining and Machine Learning (3).
Introduction to databases, data warehouses, data mining processes and techniques (e.g., predictive machine-learning models and clustering), simple text mining techniques (e.g., sentiment analysis and topic modeling) and data mining approaches for big data (e.g., MapReduce and the Hadoop ecosystem). The course focuses on the application of these techniques more than theoretical considerations. The techniques and material are presented and demonstrated using Jupyter notebooks in the Python programming language. Prerequisite(s): BSAN 710 or equivalent, or instructor's consent.
BSAN 735. Advanced Machine Learning and Deep Learning (3).
Covers advanced machine learning, natural language processing and deep learning techniques that are relevant to business applications involving high dimensional data sets, unstructured data or other complex data sets. Supervised learning, unsupervised learning, transfer learning and feature representation are all introduced in the context of real-world problems. Methods covered include deep neural networks, transformer language models, multimodal models, recurrent neural networks, convolutional neural networks, clustering, dimensionality reduction, decision trees, support vector machines and ensembles. Students use premade Jupyter and Colab notebooks (with packages such as pandas, scikitlearn, Keras, Hugging Face, and Tensorflow) to apply these techniques on topics ranging from marketing to finance to social media analytics. The assignments and project focus on applying the techniques via the provided notebooks rather than coding the models from scratch. Prerequisite(s): BSAN 734 or CS 746 or instructor's consent.
BSAN 750. Data Visualization (3).
Cross-listed as MIS 750. Introduces data visualization principles and prepares managers for developing and implementing digital performance dashboards to monitor business processes and make informed decisions. Covers a broad category of data visualization strategies for descriptive data analysis, visual data analysis and design choices. Emphasizes the importance of using big data and insightful visualizations to improve the business decision-making process. Hands-on projects with the use of modern data visualization software are included.
BSAN 760. ERP: Enterprise Resource Planning (3).
Cross-listed as DS 760. Provides students with an understanding of what Enterprise Resource Planning (ERP) systems are (also known as Enterprise Systems). ERPs are designed to assist an organization with integrating and managing its business processes by moving away from numerous disintegrated and costly legacy systems towards one main IT system for the organization. ERPs are a critical component of an organization’s IT strategy because they integrate many functions in business including operations, supply chain, sales, distribution and accounting. The course provides a technical overview of ERP systems and their managerial impact on organizations. SAP is introduced to illustrate the concepts, fundamentals, framework, information technology context, technological infrastructure and integration of business enterprise-wide applications. Latest technological trends in the ERP market are discussed. Additional accompanying software is introduced, as time permits.
BSAN 775. Introduction to Business Analytics (3).
Offers an overview of business analytics and its relationship with data analytics and data science. The course covers different analytics models at the descriptive (includes visualization), predictive and prescriptive levels, and briefly goes over ethical issues surrounding the use of such models for real-world problems. The emphasis is on business problems in various disciplines (operations, supply chain, finance, marketing, human resources, etc.). Students are exposed to various software packages available for business intelligence and analytics (e.g., Tableau, SPSS, WEKA). Topics covered in the course assist students, regardless of their background, in understanding a problem, framing the problem, selecting the proper analytical model, selecting software packages to use, running models, analyzing the results, and communicating these in a professional and effective manner. The course also includes case analyses, a term project, and discussions of emerging topics and trends in analytics.
BSAN 781. Cooperative Education (1-3).
Provides students with the opportunity to get practical experience in the field of business analytics and apply what they learned in the classroom. This course must be approved by the program director and under the supervision of a graduate faculty. May not be used for credit without prior approval of the program director. Repeatable for a total of 3 credit hours.
BSAN 790. Seminar in Special Topics (1-3).
An umbrella course created to explore a variety of subtopics differentiated by letter (e.g., 790A, 790B). Not all subtopics are offered each semester – see the course schedule for availability. Students enroll in the lettered courses with specific topics in the titles rather than in this root course.
BSAN 810. Business Acumen for Technical Professionals (3).
This class is designed for students with little to no business knowledge, who are interested in pursuing a graduate degree or profession in an analytics field. It is intended to provide these students with an introduction to key business topics that include business culture, communication, finance and accounting, marketing, operations management and supply chain, effective teams, and strategic thinking. The class delivery focuses on critical thinking, teamwork, business writing and presentation skills. Prerequisite(s): not open for MBA students or undergraduate business majors from an accredited U.S. university.
BSAN 875. Advanced Business Analytics (3).
Introduces advanced analytical techniques for different types of practical business problems. Students use PowerBI to combine data from multiple sources and create meaningful visualizations and dashboards to assist in managerial decision making. Students further build upon their knowledge of predictive and prescriptive analytics to learn advanced models including machine learning, optimization and simulation. Simulation is useful for decision making under uncertainty (e.g. disruptions in the supply chain) because it introduces risk that needs to be measured and planned for to make data driven decisions. Finally, students learn how to use different software and python packages for predictive and prescriptive analytics. Prerequisite(s): BSAN 710 and BSAN 775 or equivalent, or instructor's consent.
BSAN 885. Business Analytics Capstone (3).
Provides an opportunity for students to work on a project that draws on the skills learned from descriptive, predictive and prescriptive analytics modeling to frame a business problem, work effectively with data, visualize data, and use statistical, machine-learning or optimization models to support data-driven decision-making processes. Whenever possible, projects are based on real business problems faced by organizations in the business community. The capstone project also furthers student skills in developing business insight from quantitative analysis, knowledge of functional areas in business and/or specific industries, managing a project from start to finish, communicating with stakeholders, and using storytelling to present the final project. Pre- or corequisite(s): BSAN 735 or BSAN 875 or instructor's consent.