STATC131A
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STAT C131A - Statistical Methods for Data Science
Course Title
Statistical Methods for Data Science
Course Description
This course teaches a broad range of statistical methods that are used to solve data problems. Topics include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression, logistic regression and classification, regression trees and random forests. An important focus of the course is on statistical computing and reproducible statistical analysis. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. The R statistical language is used.
Minimum
4
Maximum
4
Grading Basis
Default Letter Grade; P/NP Option
Method of Assessment
Written Exam
Prerequisites
DATA/COMPSCI/INFO/STAT C8 or STAT 20; and MATH 1A, MATH 51, MATH 16A, or MATH 10A/10B. Strongly recommended corequisite: STAT 33A or STAT 133.
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 Replacement Courses. Upon passing, students can use the following course(s) to replace a deficient grade for this course.
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Cross-Listed Course(s)
Formats
Laboratory, Lecture
Term
Fall and Spring
Duration (in weeks)
15
Minimum Hours
3
Maximum Hours
3
Lecture Mode of Instruction
In Person
Minimum Hours
2
Maximum Hours
2
Laboratory Mode of Instruction
In Person
Minimum Hours
7
Maximum Hours
7
Term
Summer
Duration (in weeks)
8
Minimum Hours
5
Maximum Hours
5
Lecture Mode of Instruction
In Person
Minimum Hours
4
Maximum Hours
4
Laboratory Mode of Instruction
In Person
Minimum Hours
14
Maximum Hours
14