Predictive Analytics for Business Analysts: Using AI to Drive Strategy

Leonard N. Stern School of Business

What are the topics?

Leonard N. Stern School of Business

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Who should attend

Although there are no formal education or background requirements, this course is designed for executives who meet the criteria below. While we strongly encourage global participation, please note that all courses are taught in English. Proficiency in written and spoken English is required.

About the course

Predictive analytics is the key source of business value from artificial intelligence. Almost all products and services can be augmented with predictions, and as predictions become more accurate and less expensive, business leaders are redesigning their companies to take advantage of these new capabilities. Until recently, analysts needed software engineering skills to develop predictive models and to put them into operation. But a new wave of technology -- Automated Machine Learning -- has made the power of predictive analytics accessible to many decision-makers and executives.

In this course, participants will use the Microsoft Azure automated machine learning tool to build predictive models that help inform valuable business decisions. We will discuss the fundamentals of predictive analytics, learn about what kind of predictions can be made, and uncover the data is needed to make these decisions. After building various predictive models, you will then make the leap from analysis to strategy and learn how your predictions can help redesign existing product and services offerings to meet customer needs, and conceptualize new offers and business opportunities. This course combines lectures, hands-on labs and case studies to enhance learning. Because it teaches the principles underlying creation of predictions, you will be able to quickly learn to apply those principles to tools similar to the Azure tool.

Program Takeaways

Fundamentals of Predictive Analytics Developing a sound understanding of the principles of predictive analytics in order to engage with data scientists and business experts in your organization to conceptualize, build, and deploy predictive models.

Prediction Models

Analyze existing data sets and generate several models that can make useful predictions, including customer churn predictions, the probability that order fulfillment processes will fail, and which customers are likely to accept offers.

Testing Model Accuracy

Determine the accuracy of models by testing them with new data once the models are developed, and practice identifying the model(s) that should be operationalized within the company based on this assessment.

Deploying Models

Recognize the need to build decision-centric organizations to get full value from their investments in analytics

Experts

Roy Lowrance

Roy Lowrance has worked at the intersection of business strategy and technology strategy for almost 40 years. His career has been divided among strategy development at management consulting companies, leading information technology organizations, and academia. As a management consultant, he work...

J.P. Eggers

J.P. Eggers joined NYU Stern as an Assistant Professor of Management and Organizations in July 2008. Professor Eggers teaches the core M.B.A. strategy class and a strategy capstone elective. Professor Eggers's research interests focus on technological change, decision-making under uncertainty an...

Ana Del Campo

Ana Del Campo has worked in technology for over 25 years. She is a Cloud Solution Architect at Microsoft with a focus on the Education space. Advising clients that are implementing digital transformation initiatives, she adds value in three primary ways: enhancing the customer journey, increasing...

Videos and materials

Predictive Analytics for Business Analysts: Using AI to Drive Strategy at Leonard N. Stern School of Business

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Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

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