STAT215A
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Applied Statistics and Machine Learning
Course Title
Applied Statistics and Machine Learning
Course Description
Applied statistics and machine learning, focusing on answering scientific questions using data, the data science life cycle, critical thinking, reasoning, methodology, and trustworthy and reproducible computational practice. Hands-on-experience in open-ended data labs, using programming languages such as R and Python. Emphasis on understanding and examining the assumptions behind standard statistical models and methods and the match between the assumptions and the scientific question. Exploratory data analysis. Model formulation, fitting, model testing and validation, interpretation, and communication of results. Methods, including linear regression and generalizations, decision trees, random forests, simulation, and randomization methods.
Minimum
4
Maximum
4
Grading Basis
Default Letter Grade; S/U Option
American Cultures Requirement
No
Reading and Composition Requirement
None of the Reading and Composition Requirement
Prerequisite
Linear algebra, calculus, upper division probability and statistics, and familiarity with high-level programming languages. Statistics 133, 134, and 135 recommended.
Repeat Rules
Course is not repeatable for credit.
Credit Restriction Courses. Students will receive no credit for this course if the following the course(s) have already been completed.
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Formats
Laboratory, Lecture
Term
Fall and Spring
Duration (in weeks)
15
Minimum Hours
3
Maximum Hours
3
Minimum Hours
2
Maximum Hours
2
Outside Work Hours Min
7
Maximum Hours
7