Big data for managers
What you'll learn, and how you can apply it
- 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 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.
Michael Li and Ana Hocevar offer 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 utilize their input and analysis for your business’s strategic priorities and decision making.
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
Industry use cases
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
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
Tianhui Michael Li is the founder and CEO of the Data Incubator, an elite fellowship program that trains and places data scientists and quants with advanced degrees (PhD or masters) into industry roles. Previously, Michael was a data science lead with Foursquare and with Andreessen Horowitz. He holds PhD in math from Princeton University.
Ana Hocevar is a data scientist in residence at the Data Incubator, where she combines her love for coding and teaching. Ana has more than a decade of experience in physics and neuroscience research and over five years of teaching experience. Previously, she was a postdoctoral fellow at the Rockefeller University, where she worked on developing and implementing an underwater touchscreen for dolphins. She holds a PhD in physics.
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