Mar 15–18, 2020

Big data for managers

9:00am—5:00pm
Sunday, March 15—Monday, March 16
Location: 211 C

Participants should plan to attend both days of training course. Note: to attend training courses, you must be registered for a Platinum or Training pass; does not include access to tutorials on Monday.

The instructors 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.

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

  • Learn to identify and prioritize which projects a company should pursue and potential pitfalls in projects before they start
  • 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

Who is this presentation for?

Data scientists or analysts

Level

Intermediate

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

Conference registration

Get the Platinum pass or the Training pass to add this course to your package. Best Price ends January 10.

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