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AMS 561 Introduction to Computational and Data Science
This course provides a foundation of knowledge and basic skills for the successful
application in graduate research of modern techniques in computational and data science
relevant to engineering, the humanities, and the physical, life and social sciences.
It is consciously crafted to provide a rich, project-oriented, multidisciplinary experience
that establishes a common vocabulary and skill set. Centered around the popular programming
language Python, the course will serve as an introduction to programming including
data structures, algorithms, numerical methods, basic concepts in computer architecture,
and elements of object-oriented design. Also introduced will be important concepts
and tools associated with the analysis and management of data, both big and small,
including basic statistical modeling in R, aspects of machine learning and data mining,
data management, and visualization. No previous computing experience is assumed. Students
are assumed to have taken some introductory courses in two of these three math subjects:
linear algebra, calculus, and probability.
Anti-requisite: AMS 595
Pre-requisite: Requires departmental consent.
3 credits, ABCF grading
May not be repeated for credit.
Offered in the Spring Semesters
Spring Course Material:
No textbook required
Learning Outcomes: TBA