Maria Cha
Department of Statistics
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5.0
Overall Rating
Based on 2 Users
Easiness 5.0 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 5.0 / 5 How clear the class is, 1 being extremely unclear and 5 being very clear.
Workload 4.0 / 5 How much workload the class is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 5.0 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

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GRADE DISTRIBUTIONS
34.0%
28.3%
22.6%
17.0%
11.3%
5.7%
0.0%
A+
A
A-
B+
B
B-
C+
C
C-
D+
D
D-
F

Grade distributions are collected using data from the UCLA Registrar’s Office.

27.7%
23.1%
18.4%
13.8%
9.2%
4.6%
0.0%
A+
A
A-
B+
B
B-
C+
C
C-
D+
D
D-
F

Grade distributions are collected using data from the UCLA Registrar’s Office.

ENROLLMENT DISTRIBUTIONS
Clear marks

Sorry, no enrollment data is available.

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Reviews (2)

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Quarter: Winter 2024
Grade: A
Verified Reviewer This user is a verified UCLA student/alum.
April 3, 2024

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)

Helpful?

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Quarter: Winter 2024
Grade: A
Verified Reviewer This user is a verified UCLA student/alum.
April 2, 2024

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.

Helpful?

0 0 Please log in to provide feedback.
Verified Reviewer This user is a verified UCLA student/alum.
Quarter: Winter 2024
Grade: A
April 3, 2024

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)

Helpful?

0 0 Please log in to provide feedback.
Verified Reviewer This user is a verified UCLA student/alum.
Quarter: Winter 2024
Grade: A
April 2, 2024

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.

Helpful?

0 0 Please log in to provide feedback.
1 of 1
5.0
Overall Rating
Based on 2 Users
Easiness 5.0 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 5.0 / 5 How clear the class is, 1 being extremely unclear and 5 being very clear.
Workload 4.0 / 5 How much workload the class is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 5.0 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

TOP TAGS

There are no relevant tags for this professor yet.

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