Statistical Data Science II

CT Score

1500

#0 of 0 in STA

Difficulty

—/10

Workload

—h/wk

Median grade

—

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—

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No distribution published for this course.

By professor: same course, different game

PROFESSORSCOREGRADEHRS/WKTERMS

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In the tree

( STA 013 C- or better or STA 013Y C- or better; ECS 032A C- or better or ECS 032AV C- or better) or STA 032 C- or better or STA 035A C- or better or STA 100 C- or better; MAT 016B or MAT 017B { can be concurrent } or MAT 019B { can be concurrent } or MAT 021B { can be concurrent } Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Enrollment Restriction(s): During winter quarter, Pass One restricted to Data Science majors only. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL); Visual Literacy (VL). STA 035B — Statistical Data Science II (4 units) Course Description: Advanced programming and data manipulation in R. Principles of data visualization. Concepts of correlation, regression, analysis of variance, nonparametrics. Prerequisite(s): ( STA 013 C- or better or STA 013Y C- or better; ECS 032A C- or better or ECS 032AV C- or better) or STA 032 C- or better or STA 035A C- or better or STA 100 C- or better; MAT 016B or MAT 017B (can be concurrent) or MAT 019B (can be concurrent) or MAT 021B (can be concurrent). Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Enrollment Restriction(s): Winter quarter, Pass One restricted to Data Science majors only. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL); Visual Literacy (VL). This course version is effective from, and including: Fall Quarter 2026. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Enrollment Restriction(s): During winter quarter, Pass One restricted to Data Science majors only. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL); Visual Literacy (VL). STA 035B — Statistical Data Science II (4 units) Course Description: Advanced programming and data manipulation in R. Principles of data visualization. Concepts of correlation, regression, analysis of variance, nonparametrics. Prerequisite(s): ( STA 013 C- or better or STA 013Y C- or better; ECS 032A C- or better or ECS 032AV C- or better) or STA 032 C- or better or STA 035A C- or better or STA 100 C- or better; MAT 016B or MAT 017B (can be concurrent) or MAT 019B (can be concurrent) or MAT 021B (can be concurrent). Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Enrollment Restriction(s): Winter quarter, Pass One restricted to Data Science majors only. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL); Visual Literacy (VL). This course version is effective from, and including: Fall Quarter 2026→STA 035B→unlocks 12 courses

12 mapped courses list STA 035B as a prerequisite. See on the map →

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