MW 6:10pm–7:25pm · TTh 4:10pm–5:25pm
2 distinct times across 2 sections.
2 of 2 sections have room
Description
This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.
Reviews
Course experience
Aggregated across everyone who has taught this course.
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Instructor quality
n=3, 2023–2024
Aggregated across every course Dobrin Marchev has taught.
Teaching quality
2.8 / 5
Grading fairness
No signal
Workload
No signal
Difficulty
4.0 / 5 · Hard
Overall sentiment
Positive
Sections2
- 30 of 125 seats left
Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
- 2 of 125 seats left
Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
In your week
Workload and grading
- Grading
- Not published
- Format
- Not published
- Credits
- 3 credits
Reported workload
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