Curriculum Requirements

Advanced Certificate in Business Analytics

Academics > Degrees > Advanced Certificate in Business Analytics > Curriculum

Major Requirements

Business Analytics Credits:
BUSA 705 Predictive Analytics 3
Prerequisite: Prerequisites: QANT 501 or permission of the chair

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.

Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3
BUSA 710 Data Mining and Pattern Recognition for Business Analytics 3
Prerequisite: Prerequisites: MRKT 620, MIST 725 or DTSC 501

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.

Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3
BUSA 730 Practical AI for Business: Deep Learning and NLP 3
Prerequisite: Prerequisites: MRKT 620, MIST 725 or DTSC 501

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.

Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3
    Total: 9 Credits