DATA200S
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DATA 200S - Principles and Techniques of Data Science
Course Title
Principles and Techniques of Data Science
Course Description
Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.
Minimum Units
3
Maximum Units
3
Grading Basis
Default Letter Grade; S/U Option
Prerequisites
DATA/COMPSCI/INFO/STAT C8; and COMPSCI 61A or COMPSCI/DATA C88C. Corequisites: MATH 54 or ELENG 66.
Repeat Rules
Course is not repeatable for credit.
Credit Restriction Courses. Students will receive no credit for this course if following the course(s) have already been completed.
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Credit Restrictions.
Students will receive no credit for DATA 200S after completing DATA C100, or DATA C200.
Credit Replacement Courses. Upon passing, students can use the following course(s) to replace a deficient grade for this course.
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Formats
Discussion, Laboratory, Lecture
Term
Fall and Spring
Weeks
15 weeks
Weeks
15
Lecture Hours
3
Lecture Hours Min
3
Lecture Hours Max
3
Lecture Mode of Instruction
In Person, Online
Discussion Hours
1
Discussion Hours Min
1
Discussion Hours Max
1
Discussion Mode of Instruction
In Person, Online
Laboratory Hours Max
1
Laboratory Mode of Instruction
In Person, Online
Outside Work Hours Min
4
Outside Work Hours Max
5