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5.0
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1,000
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Would take again
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
CS 4371 Saquib Irtiza | |||||
B+ | |||||
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| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
CS 4371 Saquib Irtiza | |||||
B+ | |||||
Saquib Irtiza
[email protected]Grades: 99
Median GPA: B+
Mean GPA: 3.309
4
Professor rating
2
Difficulty
1
Ratings given
100%
Would take again
Introduction to Big Data Management and Analytics
CS 4371
Erik Jonsson School of Engineering and Computer Science
This course focuses on scalable data management and mining algorithms for analyzing very large amounts of data (i.e., Big Data). Included topics are: Mapreduce, NoSQL systems (e.g., key-value stores, column-oriented data stores, stream processing systems), association rule mining, large scale supervised and unsupervised learning, and applications including recommendation systems, web and big data security. 3 credit hours.
Prerequisites: (CS 2336 or CS 2337) and CS 4347.
Offering Frequency: Each year
Grades: 525
Median GPA: A-
Mean GPA: 3.434
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Saquib Irtiza
[email protected]Grades: 99
Median GPA: B+
Mean GPA: 3.309
4
Professor rating
2
Difficulty
1
Ratings given
100%
Would take again
Introduction to Big Data Management and Analytics
CS 4371
Erik Jonsson School of Engineering and Computer Science
This course focuses on scalable data management and mining algorithms for analyzing very large amounts of data (i.e., Big Data). Included topics are: Mapreduce, NoSQL systems (e.g., key-value stores, column-oriented data stores, stream processing systems), association rule mining, large scale supervised and unsupervised learning, and applications including recommendation systems, web and big data security. 3 credit hours.
Prerequisites: (CS 2336 or CS 2337) and CS 4347.
Offering Frequency: Each year
Grades: 525
Median GPA: A-
Mean GPA: 3.434
Click a checkbox to add something to compare.