Statistics of Biological Systems

Description: Lecture, three hours; laboratory, two hours. Requisite: course 30A. Designed for life sciences students. Introduction to statistics with emphasis on computer simulation of chance probabilities as replacement for traditional formula-based approach. Simulations allow for deeper understanding of statistical concepts, and are applicable to wider class of distributions and estimators. Students learn simple programming language to carry out statistical simulations, and apply them to classic problems of elementary statistics. Letter grading.

Units: 5.0
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Overall Rating 4.0
Easiness 3.2/ 5
Clarity 4.5/ 5
Workload 3.5/ 5
Helpfulness 4.8/ 5
Most Helpful Review
Winter 2023 - I thought that Professor Garfinkel was very passionate about this subject, and that normally made the lectures very engaging and interesting. He explained concepts in a way that made sense and unlike the 30 series-the coding that we did outside of class was INCREDIBLY relevant to what we were learning in class and it actually really helped to solidify my understanding of different statistical methods. The coding was actually talked about in the lectures and didn't feel like two separate lectures, as 30 a and b did. However, the workload for this class was very unreasonable. It seemed like the LS40 professors thought that this was students only class. The content was actually really interesting and I think it is very important to learn the problems with "traditional" statistical approaches. I found myself consistently spending 10-15 hours on the weekly homework and lab. It was difficult to get help because when I went to office hours, the TA was so overwhelmed with questions. Sometimes, the TAs also weren't able to give concise answers-they often seemed just as confused on the material as the students. There were two team data analysis assignments that were worth a total 20% of the grade. These assignments weren't unreasonably difficult but were a TON of work-especially considering that we also had weekly homework and a midterm on top of these assignments. I also found myself doing the majority of the work for my group, and it was frustrating because we all got the same grade and I received little help with the coding.
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Overall Rating 3.7
Easiness 3.2/ 5
Clarity 3.0/ 5
Workload 3.4/ 5
Helpfulness 3.7/ 5
Most Helpful Review
Fall 2020 - I've heard mixed reviews about this class but I'll give my take on it. Venugopal is a professor who actually tries to teach her students the concepts and I can't lie that these concepts will stick with me for a long time - it is interesting, insightful, and logical. However, be warned - this class is very (note VERY) coding heavy; this is especially true in the labs. In addition, the exams she administers are very LONG (way longer than the 3 hours she says it should take) and are vague (however, this has gotten better through the quarter and they became much more explicit). She gave us 30 points (of 1000 in the course total, so 3%) extra credit but no more as of Fall 2020. With respect to her lectures, I have to admit, I have not gone to any of her classes nor attended her lectures (with the exception of the more difficult concepts) because of time-zone differences but frankly, looking at the slides and going to lab will allow you to succeed in the class. As of Fall 2020, you're graded on 4 different categories: Labs, Homework, Knowledge Assessments, and Exams - which I will go over briefly: Labs: Tedious, but the TAs are very helpful (shoutout Alec if he's still teaching it!). The labs can be difficult to understand but they do integrate very well with what is learned in class. I recommend going to the labs synchronously as you will need to consolidate your code for your homework (if you get something wrong in the lab, then your homework will also reflect that) - NOTE as of Fall 2020, labs are not GRADED Homework: Built on the concepts learned in class and lab. Literally, most of it is copy and paste (with respect to code) but the later labs require you to make some edits in accordance to your understanding; having background knowledge in Python would be very helpful however is not needed - it will be more difficult though. Knowledge Assessments: Just to reconsolidate your information and is a good way to summarize concepts between weeks. Was graded on completion during Fall 2020 (you submit it, you get full points, hence completion) Exams: She gave us 24 hours to do them but said it should take 3. In actuality, the midterm takes around 5-8 and the final takes around 7-10 hours. The final was a LOT better structured than the midterm however, the TAs do seem to give as many points as possible and did so as long as you are thorough. Her exams, however, are poorly structured but she did listen to feedback (I think!) about the poor structures and the vagueness so this shouldn't be much of a problem. The thing is, the exams in this class are just open-ended and require you to understand the concepts to be able to complete them. Thus, you'll be writing essays for each subpart of the question to fully grasp and cover all your bases to maximize your points. (Also, points allocations for individual questions on exams do not exist in this class and are hella unclear on what constitutes to what, even in her rubric, so you just gotta cover all your bases to get as many points as you can.)
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