Sep 9–12, 2019

AI for Managers

Dylan Bargteil (The Data Incubator), Michael Li (The Data Incubator)
Monday, Sep 9 & Tuesday, Sep 10,
9:00am - 5:00pm
Location: 114

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.

This course is a non-technical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, 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

Upon completion of this course, participants will be able to apply their knowledge of data science to: 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 be able to 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...

This module is aimed at business professionals who want to learn about big data. This course is for you if: 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



Hardware and/or installation requirements:



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, neural networks

Industry Use Cases

  • Finance
  • Healthcare
  • Industrial
  • Technology

AI Within the Organization

  • Maturity levels for AI
  • Evaluating good projects for AI
  • Build vs. Buy and Hire vs. Train
  • Skills, tools, 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 Dylan Bargteil

Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with HHMI. Dylan studied physics and math at University of Maryland and holds a PhD in physics from New York University.

Twitter for thedatainc
Photo of Michael Li

Tianhui Michael Li is the founder and CEO of the Data Incubator. Michael has worked as a data scientist lead at Foursquare, a quant at D.E. Shaw and JPMorgan, and a rocket scientist at NASA. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves. He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package. Early Price ends July 26.

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