My PlannerExplore and compare past grades, professor ratings, and reviews to find the perfect class.
Grades:
Median GPA:
Mean GPA:
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Grades:
Median GPA:
Mean GPA:
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
CS 4372 (Overall) | |||||
A | |||||
CS 4372 Anurag Nagar | |||||
A | |||||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
CS 4372 (Overall) | |||||
A | |||||
CS 4372 Anurag Nagar | |||||
A | |||||
Computational Methods for Data Scientists
CS 4372
Erik Jonsson School of Engineering and Computer Science
This course will focus on the application of computational tools to solve machine learning problems. Applicable languages may include Python, 'R', Weka, or others at the discretion of the instructor. Students will use these languages to apply machine learning concepts to problem data sets. 3 credit hours.
Corequisite: CS 4375.
Offering Frequency: Each year
Grades: 429
Median GPA: A
Mean GPA: 3.709
Click a checkbox to add something to compare.
Computational Methods for Data Scientists
CS 4372
Erik Jonsson School of Engineering and Computer Science
This course will focus on the application of computational tools to solve machine learning problems. Applicable languages may include Python, 'R', Weka, or others at the discretion of the instructor. Students will use these languages to apply machine learning concepts to problem data sets. 3 credit hours.
Corequisite: CS 4375.
Offering Frequency: Each year
Grades: 429
Median GPA: A
Mean GPA: 3.709
Click a checkbox to add something to compare.