Presented By O’Reilly and Intel AI
Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

AI and data science for managers (Day 2)

Michael Li (The Data Incubator), Zachary Glassman (The Data Incubator)
Location: Continental 9
Average rating: ****.
(4.00, 2 ratings)

In this course, the instructors from The Data Incubator will be offering a non-technical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Introduction to AI and Data Science
1. Terms and definitions: what does machine learning mean?
2. Historical context and present day
3. Drivers for AI and data science
4. What’s so different about big data?
5. AI is eating the world
6. Making AI practical

Algorithms and Techniques
1. Data formats, databases, and schemas
2. Evaluating model performance and validating models
3. Terminology: regression, classification, supervised and unsupervised
4. Advanced models: random forests, support vector machines, deep learning, neural networks

Industry Use Cases
5. Finance
6. Healthcare
7. Industrial
8. Technology

AI Within the Organization
1. Maturity levels for AI
2. Evaluating good projects for AI
3. Build vs. Buy and Hire vs. Train
4. Skills, tools, platforms needed for AI
5. Structuring Data and AI Initiatives within your organization: successful and cautionary
tales

Common Pitfalls and Fallacies in AI and Data Science
1. AI and data science in the headlines: the good, the bad, and the ugly
2. Legal and regulatory implications
3. Litigation and liabilities of bad data science
4. Common fallacies in data science and AI
5. Lying with statistics and how to spot it

Photo of Michael Li

Michael Li

The Data Incubator

Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, JPMorgan, and D.E. Shaw. He’s a regular contributor to the Wall Street Journal, TechCrunch, Wired, Fast Company, Harvard Business Review, MIT Sloan Management Review, Entrepreneur, VentureBeat, TechTarget, and O’Reilly. Michael was a postdoc at Cornell, a PhD at Princeton, and a Marshall Scholar in Cambridge.

Photo of Zachary Glassman

Zachary Glassman

The Data Incubator

Zachary Glassman is a data scientist in residence at the Data Incubator. Zachary has a passion for building data tools and teaching others to use Python. He studied physics and mathematics as an undergraduate at Pomona College and holds a master’s degree in atomic physics from the University of Maryland.