SOLD OUT: Big data for managers (Day 2)

Michael Li (The Data Incubator), Gonzalo Diaz (The Data Incubator)

Location: 1A 01/02

Strata Business Summit

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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.

Description

Michael Li and Gonzalo Diaz provide a nontechnical overview of AI and data science. Learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.

Outline

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

Finance
Healthcare
Industrial
Technology

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

What you'll learn

Learn to identify potential pitfalls in projects before they start, identify and prioritize which projects a company should pursue, and translate data science insights for business professionals and decision makers
Discover how to communicate business objectives to data professionals
Understand the business implications of technical decisions and assess the risk-reward trade-offs of different projects

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.

Website

Gonzalo Diaz

The Data Incubator

Gonzalo Diaz is a data scientist in residence at the Data Incubator, where he teaches the data science fellowship and online courses; he also develops the curriculum to include the latest data science tools and technologies. Previously, he was a web developer at an NGO and a researcher at the IBM TJ Watson Research Center. He has a PhD in computer science from the University of Oxford.