In this course, the instructors from The Data Incubator will be offering 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.
Introduction to AI and Data Science
1. Terms and definitions: what does machine learning mean?
2. Historical context and present day
3. Drivers for AI and data science
4. What’s so different about big data?
5. AI is eating the world
6. Making AI practical
Algorithms and Techniques
1. Data formats, databases, and schemas
2. Evaluating model performance and validating models
3. Terminology: regression, classification, supervised and unsupervised
4. Advanced models: random forests, support vector machines, deep learning, neural networks
Industry Use Cases
AI Within the Organization
1. Maturity levels for AI
2. Evaluating good projects for AI
3. Build vs. Buy and Hire vs. Train
4. Skills, tools, platforms needed for AI
5. Structuring Data and AI Initiatives within your organization: successful and cautionary
Common Pitfalls and Fallacies in AI and Data Science
1. AI and data science in the headlines: the good, the bad, and the ugly
2. Legal and regulatory implications
3. Litigation and liabilities of bad data science
4. Common fallacies in data science and AI
5. Lying with statistics and how to spot it
Michael Li is the founder 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.
Zachary Glassman is a data scientist in residence at the Data Incubator. Zachary has a passion for building data tools and teaching others to use Python. He studied physics and mathematics as an undergraduate at Pomona College and holds a master’s degree in atomic physics from the University of Maryland.
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