4 classes matched your search criteria.
IDSC 4444 is also offered in Spring 2025
IDSC 4444 is also offered in Fall 2024
IDSC 4444 is also offered in Spring 2024
IDSC 4444 is also offered in Fall 2023
IDSC 4444 is also offered in Spring 2023
IDSC 4444 is also offered in Fall 2022
IDSC 4444 is also offered in Spring 2022
IDSC 4444 is also offered in Fall 2021
Fall 2021 | IDSC 4444 Section 001: Descriptive and Predictive Analytics (24531)
- Instructor(s)
- Class Component:
- Lecture
- Credits:
- 2 Credits
- Grading Basis:
- A-F only
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person Term Based
- Times and Locations:
First Half of Term
Tue,
Thu 01:45PM - 03:25PM
UMTC, West Bank
Carlson School of Management 1-149
- Enrollment Status:
Open (45 of 48 seats filled)
- Also Offered:
- Course Catalog Description:
- Descriptive and Predictive Analytics exposes students to a number of data mining and machine learning methods, including: exploratory methods (such as association rules and cluster analysis), predictive methods (such as K-NN and decision trees) and text mining methods. The course combines theoretical lectures with lab lectures, where the methods are practically implemented using the software R. prereq: 3001
- Class Description:
- Student may contact the instructor or department for information.
- Textbooks:
- https://bookstores.umn.edu/course-lookup/24531/1219
Fall 2021 | IDSC 4444 Section 002: Descriptive and Predictive Analytics (24618)
- Instructor(s)
- Class Component:
- Lecture
- Credits:
- 2 Credits
- Grading Basis:
- A-F only
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person Term Based
- Times and Locations:
Second Half of Term
Mon,
Wed 11:50AM - 01:30PM
UMTC, West Bank
Carlson School of Management 2-233
- Enrollment Status:
Open (45 of 48 seats filled)
- Also Offered:
- Course Catalog Description:
- Descriptive and Predictive Analytics exposes students to a number of data mining and machine learning methods, including: exploratory methods (such as association rules and cluster analysis), predictive methods (such as K-NN and decision trees) and text mining methods. The course combines theoretical lectures with lab lectures, where the methods are practically implemented using the software R. prereq: 3001
- Class Description:
- Student may contact the instructor or department for information.
- Textbooks:
- https://bookstores.umn.edu/course-lookup/24618/1219
Fall 2021 | IDSC 4444 Section 003: Descriptive and Predictive Analytics (33746)
- Instructor(s)
- Class Component:
- Lecture
- Credits:
- 2 Credits
- Grading Basis:
- A-F only
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person Term Based
- Times and Locations:
Second Half of Term
Mon,
Wed 09:55AM - 11:35AM
UMTC, West Bank
Carlson School of Management L-122
- Enrollment Status:
Open (38 of 48 seats filled)
- Also Offered:
- Course Catalog Description:
- Descriptive and Predictive Analytics exposes students to a number of data mining and machine learning methods, including: exploratory methods (such as association rules and cluster analysis), predictive methods (such as K-NN and decision trees) and text mining methods. The course combines theoretical lectures with lab lectures, where the methods are practically implemented using the software R. prereq: 3001
- Class Description:
- Student may contact the instructor or department for information.
- Textbooks:
- https://bookstores.umn.edu/course-lookup/33746/1219
Fall 2021 | IDSC 4444 Section 004: Descriptive and Predictive Analytics (33747)
- Instructor(s)
- Class Component:
- Lecture
- Credits:
- 2 Credits
- Grading Basis:
- A-F only
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person Term Based
- Times and Locations:
Second Half of Term
Tue,
Thu 01:45PM - 03:25PM
UMTC, West Bank
Carlson School of Management 2-233
- Enrollment Status:
Open (30 of 48 seats filled)
- Also Offered:
- Course Catalog Description:
- Descriptive and Predictive Analytics exposes students to a number of data mining and machine learning methods, including: exploratory methods (such as association rules and cluster analysis), predictive methods (such as K-NN and decision trees) and text mining methods. The course combines theoretical lectures with lab lectures, where the methods are practically implemented using the software R. prereq: 3001
- Class Description:
- Student may contact the instructor or department for information.
- Textbooks:
- https://bookstores.umn.edu/course-lookup/33747/1219
ClassInfo Links - Fall 2021 Information and Decision Sci Classes