Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
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Big data for managers

Michael Li (The Data Incubator), Rich Ott (The Pragmatic Institute)
Monday, March 25 & Tuesday, March 26, 9:00am - 5:00pm
Strata Business Summit
 Location: 2010
Secondary topics:  AI and machine learning in the enterprise
Average rating: ****.
(4.50, 4 ratings)

Participants should plan to attend both days of this 2-day training course. To attend training courses, you must register for a Platinum or Training pass; does not include access to tutorials on Tuesday.

Michael Li and Rich Ott offer 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 utilize their input and analysis for your business’s strategic priorities and decision making.

What you'll learn, and how you can apply it

  • Identify and prioritize which projects a company should pursue
  • Identify potential pitfalls in projects before they start
  • Communicate business objectives to data professionals
  • Understand the business implications of technical decisions and assess the risk-reward trade-offs of different projects
  • Translate data science insights for business professionals and decision makers

This training is for you because...

  • 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 Rich Ott offer 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 utilize 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

About your instructors

Photo of Michael Li

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 Rich Ott

Richard Ott obtained his PhD in particle physics from the Massachusetts Institute of Technology, followed by postdoctoral research at the University of California, Davis. He then decided to work in industry, taking a role as a data scientist and software engineer at Verizon for two years. When the opportunity to combine his interest in data with his love of teaching arose at The Data Incubator, he joined and has been teaching there ever since.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package.