Business Analytics
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| Name | Title | Credits | School |
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| BUSA 701 | Data Interaction & Visualization | 3 | School of Management |
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This course will provide students with understanding and proficiency in data interaction and visualization. Students will use tools like Tableau and Power BI for data wrangling, visualization, and dashboard design, transforming raw data into actionable insights. Emphasizing project-based learning, the course includes AI-assisted data analysis and storytelling to solve real-world business challenges and enhance decision-making. |
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| BUSA 705 | Predictive Analytics | 3 | School of Management |
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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 |
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| BUSA 710 | Data Mining & Pattern Recognition for Business Analytics | 3 | School of Management |
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This course focuses on the theoretical foundations and practical applications of unsupervised machine learning techniques to discover hidden structures and patterns in unclassified datasets. Students will explore techniques such as clustering, association rule mining, social network analysis, and collaborative filtering, with a particular focus on their real-world applications in business. Additionally, the course integrates generative AI to demonstrate how unsupervised learning can be combined with AI to automate creative business tasks such as personalized marketing and recommendation systems. This course will integrate theoretical instruction with practical, real-world business applications, using both classical and cutting-edge AI methods. Prerequisite Course(s): Prerequisites: MRKT 620, MIST 725 or DTSC 501 |
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| BUSA 715 | Social Network Analytics | 3 | School of Management |
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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 |
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| BUSA 720 | Managerial Decision Modelling | 3 | School of Management |
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This course explores advanced forecasting, simulation, and optimization techniques to support managerial decision-making across various business functions. Students will develop predictive and simulation models using AI-powered tools to address challenges in operations, marketing, and finance. Topics include time-series forecasting methods (e.g., ARIMA, machine learning-based approaches), Monte Carlo simulations, and discrete-event modeling. The course emphasizes practical applications of AI in decision modeling, through hands-on projects using Python and @Risk, to deliver impactful business solutions. Prerequisite Course(s): Prerequisites: DTSC 501/MIST 725 and QANT 620 |
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| BUSA 730 | Practical AI for Business: Deep Learning & NLP | 3 | School of Management |
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This course bridges the gap between AI theory and business practice, focusing on modern AI technologies like deep learning, natural language processing (NLP), and large language models (LLMs). Students will gain practical skills by building AI-powered business applications, such as Neural Networks and customer service chatbots. The course covers artificial neural networks (ANNs), NLP techniques, and cutting-edge AI tools like transformer-based models (e.g., BERT, GPT). By the end of the course, students will be equipped to deploy AI-driven solutions in real-world business environments. Prerequisite Course(s): Prerequisites: MRKT 620, MIST 725 or DTSC 501 |
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