Mar 15–18, 2020

Big data for managers (Day 2)

Gonzalo Diaz (The Data Incubator), Michael Li (The Data Incubator)
Location: 211 C

Who is this presentation for?

Data scientists or analysts

Level

Intermediate

Description

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

  • Structured, semistructured, and unstructured data
  • 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
  • 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

What you'll learn

  • Learn to identify and prioritize which projects your company should pursue and potential pitfalls in projects before it starts
  • Discover how to communicate business objectives to data professionals
  • Understand the business implications of technical decisions and be able to assess the risk-reward trade-offs of different projects
  • Be able to translate data science insights for business professionals and decision makers
Photo of Gonzalo Diaz

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 IBM TJ Watson Research Center. He has a PhD in computer science from the University of Oxford.

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, J.P. Morgan, and D.E. Shaw. He’s a regular contributor to the Wall Street JournalTech CrunchWiredFast CompanyHarvard Business ReviewMIT Sloan Management ReviewEntrepreneurVenture Beat, Tech Target, and O’Reilly. Michael was a postdoc at Cornell Tech, a PhD at Princeton, and a Marshall Scholar in Cambridge.

Leave a Comment or Question

Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?

Join the conversation here (requires login)

Contact us

confreg@oreilly.com

For conference registration information and customer service

partners@oreilly.com

For more information on community discounts and trade opportunities with O’Reilly conferences

Become a sponsor

For information on exhibiting or sponsoring a conference

pr@oreilly.com

For media/analyst press inquires