Business Analytics

Name	Title	Credits	School
BUSA 701	Data Interaction & Visualization	3	School of Management
This course will provide students with understanding and proficiency in data interaction and visualization. This course will build on the concepts of business statistics and cover data visualization practices and tools for presenting big data. Students will learn effective data wrangling and visualization with Tableau and other relevant tools. They will also learn to design and develop interactive dashboards for deeper insights into the data.
BUSA 705	Predictive Analytics	3	School of Management
The course provides the application of foundational topics for supervised learning algorithms such as Multiple Linear Regression, Logistics Regression, Nearest Neighbors, Decision and Regression Trees, Discriminant Analysis, Neural Networks, and Ensemble Methods. It first builds a sound understanding of data preparation, exploration, and reduction methods. This course covers prediction as well as classification processes. The emphasis is on learning the application of different machine learning techniques for decision-making situations across business domains rather than mastering the techniques' mathematical and computational foundations. Prerequisite Course(s): Prerequisites: QANT 501 or permission of the chair
BUSA 710	Data Mining & Pattern Recognition for Business Analytics	3	School of Management
This course will focus on the theoretical foundations and practical applications of unsupervised machine learning techniques, which facilitate the discovery of inherent structures and relationships within unclassified data sets. The students will learn to utilize techniques such as Clustering, Association Rule Mining, Social Network Analysis, Collaborative Filtering, and Recommendation Systems. Each subject area will be explored in depth, with a focus on algorithmic implementations and the potential applicability in various business contexts. This course will integrate theoretical instruction with practical, real-world business applications, to equip the students with a robust understanding of machine learning algorithms and the ability to leverage this knowledge in the pursuit of strategic business objectives. Prerequisite Course(s): Prerequisites: MRKT 620, MIST 725 or DTSC 501
BUSA 715	Social Network Analytics	3	School of Management
Social media plays a key role in today’s business environment for any organization. This course discusses the concepts, techniques, and tools to collect and analyze digital data available through the web and social media in organizations. It provides applied training in foundational methods of association rules, collaborative filtering, and cluster analysis. It also covers text mining and social network analytics for decision-making in different business domains in the global environment. Prerequisite Course(s): Prerequisites: DTSC 501/MIST 725 and MRKT 620
BUSA 720	Managerial Decision Modelling	3	School of Management
This course demonstrates the role of optimization and simulation models for business applications. Linear and nonlinear programming as well as discrete event simulation techniques such as Monte Carlo will be studied and applied in a variety of business disciplines such as operations, marketing, and finance. Another focus of this course is on data analytical methods for the preparation of business forecasts. Emphasis is placed upon building time series forecasting models and evaluating their accuracy. Prerequisite Course(s): Prerequisites: DTSC 501/MIST 725 and QANT 620
BUSA 730	Practical AI for Business: Deep Learning & NLP	3	School of Management
This course is designed to bridge the gap between theory and practice in business analytics through the application of deep learning techniques to solve complex business problems. Students will study Artificial Neural Networks (ANNs) and their applications to regression/classification, including image recognition. They will develop a robust understanding of statistical and probabilistic Natural Language Processing (NLP), and how these contribute to business insights such as sentiment analysis and text summarization. This course will further guide students through sequential and transformer-based NLP models, supported by case studies to provide real-world context. By course conclusion, students will be equipped with the knowledge and skills to harness the power of deep learning and NLP, and their applications in driving business decisions and strategy. Prerequisite Course(s): Prerequisites: MRKT 620, MIST 725 or DTSC 501