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Gain an Understanding of Predictive Analytics Methods

Students who complete the online Master of Science (M.S.) in Business Analytics program from the University of North Carolina Wilmington (UNCW) develop advanced skill with analyzing data, building models and generating impactful data visualizations. Students build competency in these areas through the study of common predictive analytics techniques such as regression, decision trees, random forests and neural networks.

Learning about these techniques, understanding them and using them efficiently is important in today’s competitive global business world. Company managers and executives consider some type of predictive forecast in almost every decision they make. Being able to rely on sound predictions about trends and demands of clients and consumers is a necessity.

Preparing Students for Success With Analytics Coursework

Students in UNCW’s M.S. in Business Analytics program will study an array of predictive analytics models, methods and techniques, including the following:

  • Regression is a statistical modeling approach used to examine relationships between predictor and response variables. Applications of regression techniques are wide-ranging. For example, a company may use regression to predict future demand for a product. A marketing professional may use regression to estimate the likelihood that a customer will respond to an advertisement. Understanding regression sets the stage for exploring more advanced and powerful predictive techniques.
  • Regression trees — a type of decision tree — are predictive analytics tools that use a tree-like analysis model to develop predictions. Such trees are highly intuitive, easy to visualize and can often replicate the processes that humans use to make decisions. The random forest algorithm can then be used to aggregate and leverage the power of multiple regression trees (or other decision trees) to further improve predictive accuracy.
  • Neural networks are a type of machine learning designed to mimic the structure, function and processing power of the human brain. Neural networks and related techniques such as deep learning can uncover hidden relationships within data, providing analysis that can lead to powerful insights. Many modern predictive applications and cutting-edge tools such as the large language models that underly generative AI rely on the power of neural networks.

Predictive Analytics Drives Growth and Resilience for Today’s Businesses

These and other predictive analytics techniques are vitally important if businesses, corporations and other entities are going to cope with challenges and disruptions like shifting customer demand, price-cutting maneuvers by competitors and large swings in the economy. Predictive analytics gives business executives and managers the information they need to address these issues and others effectively.

Predictive analytics techniques like generative AI continue to evolve to deal with the increasing variety and complexity of managerial forecasting challenges. Each predictive analytics method has its special use, and users must be certain they choose the right technique to address their particular situation.

The M.S. in Business Analytics program at UNCW equips students with the advanced knowledge and expertise that companies demand — and will continue to demand — in response to the growing need for predictive analytics.

Learn more about the UNCW online M.S. in Business Analytics program.

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