EC ENGR 219
Large-Scale Data Mining: Models and Algorithms
Grades
3.87
Average grade awarded in EC ENGR 219. Not a student rating: this is what the grades were.
In the A range
91%
of the 897 letter grades
D, F or W
0.2%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
0%
nobody in the record took it pass/no pass
The registrar's own records: 897 grades over 8 terms, Fall 2021 to Fall 2025. Every instructor is pooled here.
What is inside that record
No summer session is in that record.
The table below splits them, and term averages across those 8 terms ran 3.71 to 3.89.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 12 · 1.3%
- A 653 · 72.8%
- A- 153 · 17.1%
- B+ 50 · 5.6%
- B 23 · 2.6%
- B- 3 · 0.3%
- C+ 1 · 0.1%
- F 2 · 0.2%
- S 1 of 898
Percentages are of the 897 letter grades. Grey bars are non-letter outcomes: satisfactory (S).
How the non-letter outcomes are counted
They are excluded from the GPA entirely rather than scored, because counting a P as a 4.0 would be a fabrication.
By instructor
| Instructor | Predicted GPA | Raw | n | Terms | A range | Workload | Spring 2027 |
|---|---|---|---|---|---|---|---|
| ROYCHOWDHURY, VWANI P reviews ↗ | 3.87 | 3.87 | 897 | 8 | 91% | - | not teaching it |
| DING, YUANYI reviews ↗ | 3.87 | 3.87 | 32 | 1 | 94% | - | not teaching it |
Why Predicted is not the Raw average
“Predicted” is shrunk toward the course average, which is itself shrunk toward the department, so an instructor with a handful of students sits near the course mean rather than topping the list.
Grade records describe outcomes. For what a course is like to take, read student reviews written on dibs below or follow the separate external review link.
Workload and assignments
Work types reported in BruinWalk reviews of this course, across recorded instructors and terms. They may differ from this term's syllabus.
dibs has not read this course’s reviews, so it cannot say what kind of work it sets. That is dibs not having looked. It is not a course without papers or exams, and the reviews linked below are the answer until dibs catches up.
What comes first
dibs has not read a class page for EC ENGR 219 in any term it holds, so it cannot say what comes before it.
Counted in courses rather than quarters: dibs does not know which of them a department lets you take together.
In Springs
Ran in 4 of the last 4 Springs.
From the terms dibs holds, which start in Fall 2021 and exclude summer. A department can run a course in a quarter it never has before.
From the registrar's catalog
Lecture, four hours; discussion, one hour; outside study, seven hours. Introduction of variety of scalable data modeling tools, both predictive and causal, from different disciplines. Topics include supervised and unsupervised data modeling tools from machine learning, such as support vector machines, different regression engines, different types of regularization and kernel techniques, deep learning, and Bayesian graphical models. Emphasis on techniques to evaluate relative performance of different methods and their applicability. Includes computer projects that explore entire data analysis and modeling cycle: collecting and cleaning large-scale data, deriving predictive and causal models, and evaluating performance of different models. Letter grading.
Sections in Spring 2027
From the people who took it
Student reviews
What taking EC ENGR 219 was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
No student reviews are available to read on dibs for this course yet. Took it? Tell the next person what helped, what was hard, and how you spent your time.
Looking for more perspectives?
External reviews are separate and do not count toward the dibs rating.