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.)

LS40 was a mess. Do not take this class if you don't have some kind of background in statistics, this class skims lightly over key concepts and instead opts to tell you about why traditional statistics is wrong constantly. It could have been my own Zoom University fatigue but the lectures in this class were almost always painful and barely helpful. The labs were difficult and although TA's helped often , I feel like the labs should have been walked through by the TA's so the students can actually understand the homework assignments. I thought this class would be much more like LS30A and LS30B with more collaboration between students but I often felt alone and lost in this class. LS40 is pretty much under construction right now and is very disorganized and unwelcoming. Easily the worst class I have taken during my "freshman year" here at home.

P.S. Dr. V is a very nice person and taught well enough in LS30A and LS30B. She is also very accommodating for students. LS40 just does not fit her teaching style. I know she tries her best but her lectures were tough at times.

The rest of the reviewers seemed to have had a different experience than me in taking this class. Dr. Venugopal is by no means an incompetent teacher. However, she isn't the best of lecturers and after taking the exams, it sometimes felt as if she emphasized the wrong things. You are given 24 hours to complete the midterm, which was a CCLE multiple choice quiz, and 48 hours to complete the final, which was comprised of both a CCLE quiz and a coding project on CoCalc. I did not find either of the exams too difficult because I made sure I understood all aspects of the labs, which are not graded, and the homework. I missed a few points on things that I felt were not properly explained; nonetheless, I was able to get As on both of the exams.

I might be unique in saying that I enjoyed the coding aspect of the course very much. Although the coding does become somewhat repetitive in certain ways, it forces you to become comfortable with specific fundamental aspects of Python: this is immensely useful skill development. In addition, if you plan on doing research in the life sciences (as I do) in a lab or even by pursuing research as a career, the concepts conveyed in this class are paramount.

To say it briefly, this class wasn't overly trying and you can easily earn an A if you invest a reasonable amount effort.

TL;DR: This class was kind of a mess, but I still recommend taking it if you want to learn more about statistics in scientific research and the problems with some of the standard statistical practices. If you don't care at all, it won't be fun. Plus, Dr. V is very nice, wants all her students to do well, and explains the content well. I can't say how it compares to STATS13, since I know nothing about that course except it fills up quickly, but definitely consider this class since it's way less popular. Plus you pretty much don't have to do math.

Structure: This is a pretty new course, and the text book isn't even written yet. They posted a few incomplete chapters by Garfinkel(ls30 guy) and some papers by some dudes who don't like how statistics is done in research. The labs and lectures don't match up well at all, and there's concepts and things that are on the midterm/final that barely get any focus in lecture. I think Dr.V's lectures did a good job explaining the general concepts, but they didn't help too much for the labs and homework. Also the prelab videos, for the most part, are worthless. At the beginning you'll be very confused, and by the end you'll probably still be confused but a little less. If you have any problems, make sure you ask for help. Also, Jane (the other professor) and Dr. V didn't seem to be on the same page (even though their classes shared a ccle page and campuswire), and Jane referred to some of the terms in different ways than Dr. V. So be careful.

Content: I recommend at least reading the first few weeks of the written material, since p values, bootstrapping, and confidence intervals are ESSENTIAL in doing well in the later topics. I also think this class will make more sense if you actually have read a couple published studies, and are at least somewhat familiar with the statistics used. Like if you aren't familiar with seeing "p<0.05", you may be confused. I had a lot of fun learning about the bad statistics in studies, and actually understanding some of the problems. Unfortunately, I don't think the course does a very good job fully explaining the problems with traditional tests. It's more "these equations are too complicated and bad, and our simulations are so much better". Nonetheless, I think the content is very interesting. Plus, you don't really have to do any actual math. You get tested on concepts and analyzing graphs, but you never have to use any formulas. All you need to do is write the code and you'll get your p value and confidence intervals.

Labs: the labs weren't graded, but if you want to get a good grade you really should do them. Unless you're already a coding wizard, the labs are essential in actually knowing how to do the coding. Plus, in my opinion it helped me understand the concepts from lecture. I would try to do the labs before lab section, and only go to get help with the problems I couldn't solve on my own.

Homework and Creative Applications: these were our assignments. The homework was individual and doing coding stuff on cocalc, and creative application was a partner assignment that's graded on completion. The homework sometimes included stuff we never did in lab, so it was sometimes hard to figure out. They were supposed to be biweekly (one every other week), but that full apart and we ended up having a pretty long homework assignment and a partner assignment due the day of the final. (the partner assignment was making a meme. But still). I remember one of the partner assignments was pretty long, but the other ones were required very little time to complete.

Coding: I say don't worry about the coding too much. And when in doubt, ask the TAs for help. In LS30A/B I hated the coding and did not understand it at all, but even though the coding in LS40 is objectively more complicated, I found it easier. This sounds weird, but in this class the coding isn't just plotting random graphs and stuff like in LS30, it's doing the tests on data from actual studies. When doing the coding assignments, it's easy to see how what you're doing can actually be applied, which at least for me made it more fulfilling and easier to do. Keep in mind this class is python NOT sage math like LS30. It's very similar, but slightly different in very annoying ways (like srange is just range in python).

Exams: the midterm and final were a mess. The midterm had 2-3 questions that were written incorrectly, and based on a group me poll, the class did very badly. But thankfully (probably due to Dr. V), they had a "points back" opportunity, which made it a bit better. But I found the exams pretty difficult, and I think the final was harder. Dr. V was very kind and after everyone did poorly on the midterm, the final was made unlimited attempts with your highest score counting. You couldn't see your score of each attempt, but this still helped make my top final score 10% higher than my bottom score.
The final two parts with a ccle test and a coding portion. The coding portion wasn't too difficult, but it took a while to finish. If you do the labs, I think you'll be fine.

Overall: You'll learn some basic programing stuff and learn some statistics. If you despise math and seeing scary statistics formulas, this course may be for you. Though it definitely isn't easy, the course definitely isn't the hardest out there and I enjoyed it way more than LS30. It'll probably be better in the future when the text book is finished, and if it is structured in a way that makes sense.

Honestly, I wish I waited for Stats13 because this class is a whole mess. Taking a statistics class is pretty important in general especially for a stem major. This class literally taught me nothing by absolute jibberish. I didn't gain anything but lost my time trying to figure out these complicated labs that made no sense. The exams are the worst. On the questions where you can click multiple answers, if you click too many or too little, you get 50% points deducted. I missed two questions on the final and I got an 87 because of this. Like what. First, we had no preparation for the final exam. How do we know what to expect? At least a practice exam to aid a little help. I know Professor Venugopal means well and she seems very sweet, but this class is just a mess.

Save yourself the pain. Take this class with a different professor!

I think that Dr. Venugopal has a kind soul and truly cares for her students, but sometimes I did get a little lost by her lectures. I think this may have to do with the fact that it is a new course and they are still figuring out how to teach it, but I think the material here could be quite useful. Don't be scared by the thought of coding, because even though it is challenging, it is rewarding when you figure something out. I would say that if you want to be challenged, take this class.

This class was definitely one not to underestimate. If not for the adjustments made during the protests, I would definitely not have done as well. the bulk of the work is the homework, which is done on Cocalc. It is rarely easy to do and can take 4-5 hours to complete. You will also most likely need to ask a TA or another person for help at multiple points to try to start this as soon as possible so you can hit up a TA during OH and get the help you need. The number of stressed out Sunday mornings I've spent scrambling together a homework assignment before it was due on noon....not worth it!! The midterm is also fairly rough if you were not paying attention to lecture. We had 24 hours to work on it. Don't forget to do the pre-lab quizzes!! The knowledge assignments are fairly easy as well but time consuming (hit me up at ********** if you want mine to save time). EC is given for practice exams submitted.

I have mixed feelings about this class. Dr. V seems like a really sweet person and I really appreciate the accommodations she made for her students this quarter, but at the same time I wish the lectures were a little more organized. For example there were a couple of days where we had to repeat a previous lecture because something was taught or explained wrong, so to me it wasn't always the best use of time. She is definitely helpful for answering questions and the material wasn't too difficult, so it wasn't too bad overall.

A lot of accommodations were made this quarter due to remote learning and just the general chaos of the world during finals, but this class turned out better than I expected! Before taking LS 40, I was disinterested in statistics and was unenthusiastic about the prospect of learning about it for an entire quarter. I was afraid of the coding assignments because I struggled with the ones in LS 30 A/B. But looking back, LS 40 was surprisingly not as hard or boring as I imagined. Prof. Venugopal made sure lectures were a safe space to ask questions and loved her students dearly. Sometimes topics were a bit unclear, but students managed to ask questions and get the clarity needed.

Contentwise, I had no idea what bootstrapping and box models were, let alone how to code for them. But I think the class was pretty well organized and topics built from previous lectures. First, we started out discussing basic things like plots, p-values, and central measures of tendency. Later in the course, we applied this to theoretical simulations and this was a part of every kind of statistical testing we explored.

There are 6 components in the syllabus that awarded points: homework, knowledge assessments, prelab quizzes, midterm, final, and extra credit. Coding labs are NOT a part of your grade!

There is a weekly homework coding assignment, and I suppose this is the hardest part of each week, but there were only a couple difficult ones. I highly recommend going to TA office hours and asking questions on how to do something if you're stuck, or after you're finished with your coding lab you can ask your TA or LA then. Homework was 36% of our grade (9 homeworks, 40 points each).

Every two weeks or so we would be assigned these "Knowledge Assignments". These vary from answering questions to making a short PowerPoint or decision tree about the statistical topic assigned. If you team up with a partner, you are supposed to be awarded extra credit for doing so. So each time, everyone would partner up and work on it. This was a little more difficult because we were not face-to-face and communication was important, but this is very doable and easy. Just pick a partner that you trust will get the work done on time. In total, this category is only worth 5% of your grade, but I'm pretty sure everyone gets all the points if you just answer the questions correctly.

Each week we also answered a short, 5 multiple-choice question quiz before attending our coding lab, all of which was 4% of our grade. This was super easy and all the info could be found by scrolling through reading the instructions of the lab on Cocalc. I think the quizzes were just for participation and weren't necessarily graded on correctness as well. The coding labs, not graded and just there as a review/practice each week, were SO much easier than the ones in previous classes like LS 30A/B because there are instructions for each question. Sometimes it will tell you what new formula to use, syntax tips, etc. It's easy to finish most labs in about an hour. I don't know if the instructions were provided to us just because of remote learning, but either way this made the labs very doable and efficient. Labs built upon previous labs too, and were reflective of what we went over that week in class.

There was only one midterm for this class worth about 20% (200 points) of our grade, and it was on Cocalc. Most of the questions were free response and more conceptual, asking more about the "why" of the statistical tests we used than the "what". There was only 1 coding problem at the end, but it was super easy and it was similar to previous lab problems we had done. We were also allowed to reference lectures/anything on CCLE, previous labs, and homework during the exam so that was helpful.

The final exam, worth 35% of our grade, was boosted automatically to 100% for all students this quarter. I didn't look at it, but I've heard that it was a similar format to the midterm and would've been doable.

There is also some extra credit available, like the Mid-Quarter LA survey, course evaluations at the end of the quarter, or practice midterm and practice final exam submissions.

It's pretty wasy to get an A in this class if you go to office hours, attend lectures, and just stay organized. There's not too much work but put in the effort to study and remember what the professor went over and you should be good for the exams. Dr. V. was a wonderful professor and I highly recommend taking her!