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 networks
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 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
Tim Schwuchow is a data scientist in residence at the Data Incubator. Previously, he designed and instructed several undergraduate- and graduate-level courses at Duke, was an analyst at quantitative hedge fund D. E. Shaw & Co., and was an economist for the Postal Regulatory Commission. He holds degrees in economics from Harvard and Duke.
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