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Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
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Amazing professor who I will be trying to take for every class she teaches. She is so clear and effective in her teaching. All of her homeworks align with the material and sufficient guidance is provided for them. There is a group project that allows you to showcase your new skills and meet other students. The exams are very fair and modeled well off of the lecture material and homework assignments. Overall I would recommend this class with this professor wholeheartedly.
Grading Scheme:
20% homework (5x4% each - 1 dropped)
20% group project
35% exam (the one with the higher grade)
25% other exam (the one with the lower grade)
Maria Cha is the best professor in the Stats department. Even better than Miles Chen. She is super nice, with very clear slides, case studies, and examples(!!!).
The grading scheme: 20% HW, 20% Group Project/Presentation, 25% midterm and 35% final OR 35% midterm and 25% final.
Homework: REALLY LONG. But doable. Discussions are optional and spent giving all the HW answers. Still do the hw though since the tests are similar. HW was mostly in R but also some by hand. Although you won't be tested on coding, you will need to know the contents/meaning of output tables in R like the back of your hand.
Group project: Simple data analysis and regression of a data set. Essentially use the code from HW on this project, make pretty slides, and slap together a 6 page report. Presentation was graded on completion, and pretty much all groups got a 100% on the final report.
Tests: Averages of ~90% for both. Most people finished the final in ~25 minutes. Very little math on either tests, but instead analyzing R output, taking information from an r output table and plugging it into an equation, and analyzing/explaining different graphs.
This class has incredibly useful content if you want to get into any sort of analytics/data science field. The p value is <0.05 which means the null hypothesis that this is a hard class, can be rejected, and we accept the alternate hypothesis that this class is an easy A.
Amazing professor who I will be trying to take for every class she teaches. She is so clear and effective in her teaching. All of her homeworks align with the material and sufficient guidance is provided for them. There is a group project that allows you to showcase your new skills and meet other students. The exams are very fair and modeled well off of the lecture material and homework assignments. Overall I would recommend this class with this professor wholeheartedly.
Grading Scheme:
20% homework (5x4% each - 1 dropped)
20% group project
35% exam (the one with the higher grade)
25% other exam (the one with the lower grade)
Maria Cha is the best professor in the Stats department. Even better than Miles Chen. She is super nice, with very clear slides, case studies, and examples(!!!).
The grading scheme: 20% HW, 20% Group Project/Presentation, 25% midterm and 35% final OR 35% midterm and 25% final.
Homework: REALLY LONG. But doable. Discussions are optional and spent giving all the HW answers. Still do the hw though since the tests are similar. HW was mostly in R but also some by hand. Although you won't be tested on coding, you will need to know the contents/meaning of output tables in R like the back of your hand.
Group project: Simple data analysis and regression of a data set. Essentially use the code from HW on this project, make pretty slides, and slap together a 6 page report. Presentation was graded on completion, and pretty much all groups got a 100% on the final report.
Tests: Averages of ~90% for both. Most people finished the final in ~25 minutes. Very little math on either tests, but instead analyzing R output, taking information from an r output table and plugging it into an equation, and analyzing/explaining different graphs.
This class has incredibly useful content if you want to get into any sort of analytics/data science field. The p value is <0.05 which means the null hypothesis that this is a hard class, can be rejected, and we accept the alternate hypothesis that this class is an easy A.
Based on 2 Users
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