Graduate Certificate in Retail Consumer Predictive Analytics

• Foundations of Predictive Analytics: Introduction to predictive analytics, data mining, and machine learning; understanding various predictive modeling techniques and their applications.

• Data Management for Retail Analytics: Data collection, cleaning, and management for retail consumer data; database systems and data warehousing concepts.

• Statistical Methods in Consumer Analytics: Inferential statistics, probability distributions, and hypothesis testing; regression analysis and analysis of variance for retail consumer data.

• Retail Consumer Behavior: Understanding consumer preferences, decision-making, and purchasing patterns; the role of demographics, psychographics, and lifestyle factors in retail consumer behavior.

• Predictive Modeling for Retail Consumers: Development and implementation of predictive models for retail consumer behavior; model evaluation and selection.

• Machine Learning Techniques in Retail Analytics: Supervised and unsupervised learning techniques; neural networks, decision trees, and ensemble methods in retail consumer predictive analytics.

• Retail Data Visualization and Reporting: Data visualization techniques and tools for retail consumer analytics; creating effective reports and presentations for stakeholders.

• Ethical Considerations in Consumer Analytics: Understanding ethical considerations in consumer analytics; data privacy, security, and informed consent.