AI for managers
9:00am - 5:00pm
What you'll learn, and how you can apply it
- Identify and prioritize which projects your company should pursue and identify potential pitfalls in your projects before you start
- Learn to communicate business objectives to data professionals and translate data science insights for business professionals and decision makers
- Understand the business implications of technical decisions
- Be able to assess the risk-reward trade-offs of different projects
Who is this presentation for?
- You're a business professional who wants to learn about big data.
- You work with data scientists or analysts regularly.
- You manage teams or projects with a significant data component.
- You find yourself translating between data and management.
Introduction to AI and data science
- Terms and definitions: What does machine learning mean?
- Historical context and present day
- Drivers for AI and data science
- What’s so different about big data?
- AI is eating the world
- Making AI practical
Algorithms and techniques
- Data formats, databases, and schemas
- Evaluating model performance and validating models
- Terminology: Regression, classification, supervised, and unsupervised
- Advanced models: Random forests, support vector machines, deep learning, and neural networks
Industry use cases
AI within the organization
- Maturity levels for AI
- Evaluating good projects for AI
- Build versus buy and hire versus train
- Skills, tools, and platforms needed for AI
- Structuring data and AI initiatives within your organization: Successful and cautionary tales
Common pitfalls and fallacies in AI and data science
- AI and data science in the headlines: The good, the bad, and the ugly
- Legal and regulatory implications
- Litigation and liabilities of bad data science
- Common fallacies in data science and AI
- Lying with statistics and how to spot it
About your instructor
Nicholas Cifuentes-Goodbody is a data scientist in residence at the Data Incubator. He’s taught English in France, Spanish in Qatar, and now data science all over the world. Previously, he was at Williams College, Hamad bin Khalifa University (Qatar), and the University of Southern California. He earned his PhD at Yale University. He lives in Los Angeles with his amazing wife and their adorable pit bull.
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