UC_DAVIS · STA · COURSE SHEET
STA 141C4 creditsSee offeringsHas prerequisites
Big Data & High Performance Statistical Computing
CT Score
1500
#0 of 0 in STA
Difficulty
—/10
Workload
—h/wk
Median grade
—
from grade distribution
Would take again
—
n = 0 verified
Grade distribution
No distribution published for this course.
By professor: same course, different game
PROFESSORSCOREGRADEHRS/WKTERMS
Professor ratings →Sections
No section data for this term yet.
In the tree
STA 141B C- or better or ( STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better or ECS 032AV C- or better)). Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics; open to all students during Open registration. Grade Mode: Letter. STA 141C — Big Data & High Performance Statistical Computing (4 units) Course Description: High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. Prerequisite(s): STA 141B C- or better or ( STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better or ECS 032AV C- or better)). Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. This course version is effective from, and including: Fall Quarter 2026. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics; open to all students during Open registration. Grade Mode: Letter. STA 141C — Big Data & High Performance Statistical Computing (4 units) Course Description: High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. Prerequisite(s): STA 141B C- or better or ( STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better or ECS 032AV C- or better)). Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. This course version is effective from, and including: Fall Quarter 2026→STA 141C→unlocks 0 courses
No mapped courses require this one yet. See on the map →
Study groups
0 active🔒 Verified-only writes. You post as “sophomore, lecture section”: real enough to trust, anonymous enough to be honest.