Sep 9–12, 2019

AI for managers

Nicholas Cifuentes-Goodbody (The Data Incubator)
Monday, Sep 9 & Tuesday, Sep 10,
9:00am - 5:00pm
Location: Market
Secondary topics:  Ethics, Security, and Privacy
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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.

Dylan Bargteil and Michael Li lead you through a nontechnical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls. 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.

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
  • Communicate business objectives to data professionals
  • Understand the business implications of technical decisions
  • Be able to assess the risk-reward trade-offs of different projects
  • Translate data science insights for business professionals and decision makers

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.

Level

Beginner

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 instructor

Photo of Nicholas Cifuentes-Goodbody

Nicholas has taught English in France, Spanish in Qatar, and now Data Science all over the world! He completed his PhD at Yale University and, before coming to TDI, worked at Williams College, Hamad bin Khalifa University (Qatar), and the University of Southern California. He lives in Los Angeles with his amazing wife and their adorable pit bull.

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