Business Knowledge Series course

Presented by Eric Siegel, Ph.D., founder of Predictive Analytics World, author of Predictive Analytics, and former Columbia University professor.

• Accessible to business learners and yet vital to techies as well


• A vendor-neutral, universally applicable curriculum


• Equivalent to a full-semester MBA or graduate-level course

Machine learning is booming. It reinvents industries and runs the world. According to Harvard Business Review, machine learning – also known as predictive analytics – is “the most important general-purpose technology of our era.”

But while there are so many how-to courses for hands-on techies, there are practically none that also serve the business leadership of machine learning. This is a striking omission since success with machine learning relies on a very particular project leadership practice just as much as it relies on adept number crunching. Without that leadership, most machine learning projects fail.

By filling that gap, this course empowers you to generate value with machine learning, whether you are a techie, a business leader, or some combination of the two. It delivers the end-to-end expertise that you need, covering both the core technology and the business-side practice.

Why cover both sides? Because both sides need to learn both sides! Everyone leading or participating in the deployment of machine learning must study them both.

Beyond the core tech. As with most machine learning courses, you'll learn how the technical methods work “under the hood” – in an accessible way that's understandable to all learners. But you'll also continue beyond that to master critical business-side best practices that are usually omitted.

Learn how to

• Apply: Identify business opportunities for applying machine learning, to spike sales, accumulate clicks, fight fraud, and deny deadbeats.
• Plan: Determine how machine learning will drive operations, the staffing requirements to get there, and the projected win in terms of profit or ROI – and then internally sell the project, gaining buy-in from your colleagues.
• Lead: Manage or participate in the end-to-end implementation of machine learning, from the generation of predictive models to their launch into production.
• Watch your step: Circumvent the prevalent, treacherous pitfalls that otherwise derail machine learning projects and quell the overhype of “artificial intelligence.”
• Prep the data: Formulate the data requirements, which rely heavily on business priorities, and describe them in both technical and management-level language.
• Regulate: Foresee and mitigate ethical pitfalls, the risks to social justice that stem from machine learning (also known as AI ethics or equitable algorithms).

Who should attend

Anyone who wants to participate in the value-driven use of machine learning, no matter whether you will do so in the role of enterprise leader or quant. Since there is no hands-on and no heavy math (other than one spreadsheet-based exercise, as well as optional hands-on opportunities with SAS software), this program serves business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants. But technical learners should take another look. Before jumping straight into the hands-on, as data scientists are inclined to do, consider one thing: This holistic curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact. This course is also a good fit for university students, including those enrolled in an MBA program.

Formats available	Standard Duration
e-Learning:	30 hours/180 day	System Requirements

This curriculum is fully accessible to all types of learners on both the business and technical side, including non-technical business managers and newcomers, although it is delivered at a level that assumes a college degree or higher.

No background in statistics or programming is required. There is one hands-on exercise using Microsoft Excel or Google Sheets, but there are no required exercises involving coding or the use of machine learning software.

Although this course dives deeply into technical concepts as extensively as possible, it does so only in a way that remains relevant and understandable to non-technical learners. You will not need to follow heavy math – not much more than ratios and some graphical visualizations – to get a firm grasp of how machine learning methods work.

This course addresses SAS Viya software.

This course is actually three (3) courses in one. When you enroll, you'll be given access to the entire three-course series – also referred to, overall, as a specialization.

COURSE 1 OF 3: THE POWER OF MACHINE LEARNING

Course Series Overview

• Topics covered and learning objectives.
• Why this course is not "hands-on" and why it is still good for techies anyway.
• About your instructor.

The Impact of Machine Learning

• Defining machine learning and predictive analytics.
• The profit of prediction.
• Applications of machine learning.
• A great evolutionary step.

Data: The New Oil

• Discoveries: what data tells us.
• Gaining insights from training data.
• First steps of predictive modeling.

Predictive Models: What Gets Learned from Data

• Predictive modeling.
• Deployment: Predictive models take action.
• The best that machine learning can get: potential and limits.

Industry Perspective: AI Myths and Real Ethical Risks

• Hype versus reality: AI and machine learning.
• Ethics: With great power comes great responsibility.

COURSE 2 OF 3: LAUNCHING MACHINE LEARNING

Business Applications of Machine Learning

• Course introduction.
• Marketing and sales applications.
• Risky business: financial apps and fraud detection.

Scoping, Greenlighting, and Managing Machine Learning Initiatives

• The leadership process: how to manage machine learning projects.
• Project scoping and greenlighting.

Data Prep: Preparing the Training Data

• Targeting prediction: the dependent variable.
• Fueling prediction: the independent variables.

The High Cost of False Promises, False Positives, and Misapplied Models

• Pitfalls: misjudging and miscommunicating predictive performance.
• The accuracy fallacy (major pitfall).
• Ethics: regulating how models affect lives.

COURSE 3 OF 3: MACHINE LEARNING UNDER THE HOOD

The Foundational Underpinnings of Machine Learning

• Course introduction.
• P-hacking (major pitfall).
• Overfitting (major pitfall).
• Correlation does not imply causation (major pitfall).
• The principles of predictive modeling.

Standard, Go-To Machine Learning Methods

• Decision trees.
• Naive Bayes.
• Linear models and perceptrons.
• Why probabilities drive better decisions than yes/no outputs.
• Logistic regression.

Advanced Methods, Comparing Methods, and Modeling Software

• Neural networks.
• Deep learning.
• Ensemble models.
• Compare and contrasting machine learning methods.
• Machine learning software: how tools vary and how to choose one.
• Uplift modeling (also known as persuasion modeling).

Pitfalls, Bias, and Conclusions

• Machine bias.
• Explainable machine learning and model transparency.
• Pitfalls summary: the seven deadly sins of machine learning.
• Conclusions and what's next: continuing your learning.