Overview
Subject to validation
Make smarter, data-driven decisions with the power of predictive analytics and machine learning. Explore the math and models behind today’s most successful decisions and learn how to turn raw data into insights that drive real-world impact.
Starting in fall 2026.
Teaching Modality
Duration
Price
Award
Key benefits
Develop advanced analytical skills
Build a strong understanding of the mathematical, statistical, and algorithmic models that power modern decision-making across industries.
Gain hands-on experience with industry tools
Develop practical experience using analytical methods and open-source technologies to solve complex business and operational challenges.
Strengthen strategic thinking
Learn to weigh risks, test models, and balance costs so you can make bold, informed decisions that move your organization forward.
Who should enroll?
Organizations are increasingly relying on predictive analytics and artificial intelligence to make faster, smarter, and more informed decisions. Our Professional & Executive Education program is designed for professionals who want to leverage data to drive better business outcomes.
- Individuals interested in predictive analytics, machine learning, and the power of data-driven decision-making
- Professionals working with large datasets who want to master advanced forecasting and modeling skills
- Managers and leaders responsible for strategic planning, operations, or organizational performance
Have questions?
Got a question? Get in touch with us.
FAQs
What is the difference between predictive analytics and machine learning?
Predictive analytics uses historical and current data to estimate what is likely to happen next. Machine learning uses algorithms to identify patterns in data and make predictions or decisions.
The two often work together. Machine learning can power a predictive model, while predictive analytics connects that model to a specific business question or decision.
Our Advanced Decision Making: Predictive Analytics and Machine Learning program brings both areas together. It helps you understand how you can support smarter, data-driven decisions.
Do I need coding or advanced statistics experience to take a predictive analytics course?
You do not need to be a machine learning engineer to benefit from a predictive analytics course. Confidence with data, spreadsheets or basic statistics can make the concepts easier to apply.
Programming knowledge becomes more important if you want to build models independently. However, managers can also benefit by learning how to question assumptions, interpret results and evaluate whether a model is suitable for a decision.
How are predictive analytics and machine learning used in the workplace?
Organizations use predictive models to forecast demand, identify financial or operational risk, anticipate customer behavior, plan resources and detect unusual patterns.
The Advanced Decision Making: Predictive Analytics and Machine Learning program can help you understand how these models produce results. Business knowledge is also needed to decide whether a result is relevant, reliable and appropriate to use.
Is predictive analytics useful for managers who do not work in data roles?
Yes. Managers increasingly need to understand how forecasts and automated recommendations are created, even when analysts or technical teams build the models.
Knowledge of data-driven decision-making helps leaders ask better questions, recognize weak evidence and combine analytical results with professional judgement. This means they can assess an output rather than accepting it without challenge.
How can organizations use predictive models responsibly?
Responsible use starts with relevant data, transparent objectives and checks for bias, privacy risks and model error.
Teams should monitor whether a model continues to perform as conditions change and make clear where human review is required. This is especially important when predictive analytics and machine learning influence decisions affecting customers, employees or access to services.