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Foundations of Data Science & Artificial Intelligence
Engineering & Math Program
Tokyo, Japan
Dates: late May 2027 - late Jul 2027
Foundations of Data Science & Artificial Intelligence
OVERVIEW
CEA CAPA Partner Institution: CEA CAPA Tokyo Center
Location: Tokyo, Japan
Primary Subject Area: Computer Sciences
Instruction in: English
Course Code: CS310TYO
Transcript Source: University of New Haven
Course Details: Level 300
Recommended Semester Credits: 3
Contact Hours: 45
Prerequisites: None. No prior programming or data science experience is required. Curiosity about data, technology, and artificial intelligence is helpful.
DESCRIPTION
This course offers an accessible but rigorous introduction to the foundations of data science and artificial intelligence. It is designed for students with limited or no prior technical background who want to understand how data is used to generate insight, support decisions, and drive modern digital applications. The course covers key stages of the data science workflow, from data collection, cleaning, and exploration to visualization, interpretation, and introductory predictive analytics. Students are introduced to Python as an applied tool for inspecting, analyzing, and visualizing data, and develop their skills through practical exercises and a capstone project.
The course also builds foundational literacy in machine learning and artificial intelligence, including supervised and unsupervised learning, regression, classification, clustering, and model evaluation. Students explore current AI tools, including large language models, and examine how such tools can support data work while requiring verification, critical judgment, and responsible use.
The course also builds foundational literacy in machine learning and artificial intelligence, including supervised and unsupervised learning, regression, classification, clustering, and model evaluation. Students explore current AI tools, including large language models, and examine how such tools can support data work while requiring verification, critical judgment, and responsible use.