ECON 434
Machine Learning and Big Data for Economists
Grades
3.75
Average grade awarded in ECON 434. Not a student rating: this is what the grades were.
In the A range
79%
of the 193 letter grades
D, F or W
0.0%
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: 193 grades over 4 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 4 terms ran 3.42 to 3.96.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 40 · 20.7%
- A 88 · 45.6%
- A- 24 · 12.4%
- B+ 17 · 8.8%
- B 10 · 5.2%
- B- 10 · 5.2%
- C+ 4 · 2.1%
Percentages are of the 193 letter grades. Grey bars are non-letter outcomes.
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 | Fall 2026 |
|---|---|---|---|---|---|---|---|
| CHETVERIKOV, DENIS NIKOLAYEVICH reviews ↗ | 3.74 | 3.75 | 193 | 4 | 79% | - | 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 ECON 434 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 Falls
Has not run in Fall in the 4 Falls dibs holds records for, back to Spring 2022. That is 4 years of grade records rather than the course catalog, so it says what has happened and not what the department will do next.
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, three hours; discussion, one hour. Limited to Master of Applied Economics students. Discussion of some machine learning techniques including lasso, regression trees, random forests, and neural networks. Covers most recent developments at intersection of machine learning and econometrics, now commonly referred to as double machine learning. Study of double machine learning in detail, and discussion of how to apply it to enhance analysis of classical econometric problems, such as program evaluation and demand estimation. Letter grading.
Sections in Fall 2026
From the people who took it
Student reviews
What taking ECON 434 was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
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