SOLD OUT: Big data for managers (Day 2)
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.
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.
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
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
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.
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.
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