Hartman was nice but she seemed pretty inexperienced. Her class setup was focused on peer learning so she and the TAs put us in groups that we stayed in for the entire quarter. Everything was done with our groups: the homework, the classwork, and our final project. There was also an enormous amount of coding in the R language which I found pretty hard because she didn't really teach it to us; she just sent us R files with instructions on how to do certain things.

A normal lecture would consist of Hartman talking about what we'd be doing that day and then we'd work with our groups on a worksheet. Her lectures weren't effective for me because I felt like she wasn't really teaching. She was just giving us basic information about whatever stats topic and then we would have to put it to work in R and on our worksheets.

I understand what she was trying to do with the whole group learning thing but I feel like it actually hindered my learning. I think the group situation allows people to slide by and not actually learn the material because we can just rely on others in our group to keep us afloat. I wasn't the only one who was lost in my group but we all sort of did what we could to get the work done. I think the class was curved at the end of it all because people were struggling. She also offered extra credit. If I remember correctly, I think it was .5% for reading an entire book and writing a 3 page summary or reflection on it. I definitely didn't think that was worth it.

Overall, I had a bad time in this class but I survived. Unless Hartman changes the way she runs her class, I'd recommend a different professor.

The statistics aspect of Poli Sci 6 is easy, so long as you've taken statistics before or are willing to do a bit of independent practice using Open Intro Statistics (the free online textbook associated with this course). This makes the midterm and the final reasonably manageable.

What makes this course difficult is the coding , which is a significant aspect of the problem sets and project you'll complete in groups. Coding can't really be taught; instead, it is completed by modeling the examples in the program boot camps you'll go through in the first weeks of class. This is time consuming, but easy enough once you know where to look for the examples. The real problem with this course lies in the fact that coding is also not at all conducive to group work. The amount of work you have to do per problem set (there are four that you'll complete with your group) is largely dependent on the ability of the group mates you are randomly assigned, as well as their willingness to split up the work evenly. In my case, I ended up doing most of the coding for every problem set, so this course constituted about half of my overall workload at UCLA.

I like Professor Hartman. I like the TA I had. I really liked the subject material. Despite all of this, I cannot recommend this course to anyone. While the actual material is mostly basic statistics, the amount of work Professor Hartman gave was way too much to complete within (usually) a week. Lectures weren't helpful at all and I ended up learning everything either by myself or with my TA. Many instructions within both the homework and the in-class worksheets contained typos that, at worst, made some questions impossible to answer without someone emailing Professor Hartman about the problem. While I do think the idea of a group dynamic can be beneficial, the way it was executed was poor and sloppy, allowing people to ride on others' work without actually learning. Many of my classmates were ill-prepared to learn R, and we were given very little guidance outside of the first two lectures. Even my groupmate, who attended nearly every office hour for my TA, did not understand many of the functions we didn't have to learn. As it stands, this is a class where you have a lot of potential to learn, but the workload as well as the lack of direction makes it one course you should avoid.

This will be the most difficult class that you will take as a Political Science major. There is an extensive amount of work required from you as an individual as well as from your groupmates. If you do not have excellent groupmates who know how to code in R studio, the class will be extremely difficult and you will most likely receive a low grade. Professor Hartman has a hands-off approach to teaching, because the TAs are there for the class. However, their hours are stretched thin, because you will find everybody from your class asking for help and quite possibly, crying about how hard the coding is for the rest of the quarter. There is statistics involved, but it is not about memorizing formulas. Professor Hartman wants you to learn about the concepts and connect past lectures to new lectures after the midterm. It is all connected so if you lag behind on a chapter, you will not understand the next. About half of the class dropped after they received their grades back from the first coding assignment. This class really tests your patience so be prepared!

There is no doubt that Professor Hartman is knowledgeable in her field. However, the massive amount of content - both with R coding and raw statistics - makes this class feel rushed and haphazard.

As a coding neophyte, I found learning R to be an utter nightmare. The experience was extremely hands off and learning is peer driven. Again, I believe this is a function of damning time constraints rather than indifference on the part of Professor Hartman.

The class starts as a programming boot camp, but eventually morphs into roughly the average statistics course. If it is any consolation, the coding and handwritten homework are much harder than the midterms or final exams. What is more, she is very charitable with the extra credit.

I suppose the TA roster may be volatile and subject to change, but the TAs I interacted with were generally inaccessible and disinterested. Given Hartman is pretty hands off, TA help is always in high demand and they are often spread thin.

I did not find this course to be all that difficult, but there it comes with a considerable workload and the pangs of computer interaction. Be ready to commit.