Big data for managers
Sunday, March 15—Monday, March 16
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?
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
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
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