Upon completion of this course, participants will be able to apply their knowledge of data science to:
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, neural
Industry Use Cases
AI Within the Organization
Maturity levels for AI
Evaluating good projects for AI
Build vs. Buy and Hire vs. Train
Skills, tools, 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
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. Formerly, Michael served as a data science lead with Foursquare and with Andreessen Horowitz. He holds PhD in Math from Princeton University.
Russell Martin is a Data Scientist in Residence at The Data Incubator. He received his PhD in Applied Mathematics from the Georgia Institute of Technology. Russ lived and worked in the UK for seventeen years, including at Warwick University and the University of Liverpool, where he taught in the Department of Computer Science. As a Data Scientist in Residence, Russ instructs Fellows in our Data Science Fellowship, teaches online courses, and leads trainings with our corporate partners.
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