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Spring 2025

 

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Data Science (DSCI)
204 Pacific Hall,
College of Arts & Sciences
8 - No cost for class textbook materials.
Course Data
  DSCI 102   Foundat Data Science 2 4.00 cr.
This course expands upon critical concepts and skills introduced in DSCI 101. Topics include the normal distribution, confidence intervals, regression, and classifiers. Sequence with DSCI 101.
Grading Options: Graded for all students
Instructor: Muehleisen AE-mail
See CRN for CommentsPrereqs/Comments: Prereq: DSCI 101, MATH 101 (or equivalent placement score) or any other college-level math course.
Course Materials
 
  CRN Avail Max Time Day Location Instructor Notes

Lecture

31541 42 100 0830-0950 tr 110 FEN Muehleisen A !8

Final Exam:

0800-1000 m 6/09 110 FEN
 
Associated Sections

+ Lab

31542 17 25 0900-0950 f 104 CON Delevich C  

+ Lab

31543 4 25 1000-1050 f 104 CON Delevich C  

+ Lab

31544 10 25 1200-1250 f 301 CON Ranjitha Rani R  

+ Lab

31545 11 25 1300-1350 f 104 CON Ranjitha Rani R  
Academic Deadlines
Deadline     Last day to:
March 30:   Process a complete drop (100% refund, no W recorded)
April 5:   Drop this course (100% refund, no W recorded; after this date, W's are recorded)
April 5:   Process a complete drop (90% refund, no W recorded; after this date, W's are recorded)
April 6:   Process a complete withdrawal (90% refund, W recorded)
April 6:   Withdraw from this course (100% refund, W recorded)
April 7:   Add this course
April 7:   Last day to change to or from audit
April 13:   Process a complete withdrawal (75% refund, W recorded)
April 13:   Withdraw from this course (75% refund, W recorded)
April 20:   Process a complete withdrawal (50% refund, W recorded)
April 20:   Withdraw from this course (50% refund, W recorded)
April 27:   Process a complete withdrawal (25% refund, W recorded)
April 27:   Withdraw from this course (25% refund, W recorded)
May 18:   Withdraw from this course (0% refund, W recorded)
Caution You can't drop your last class using the "Add/Drop" menu in DuckWeb. Go to the “Completely Withdraw from Term/University” link to begin the complete withdrawal process. If you need assistance with a complete drop or a complete withdrawal, please contact the Office of Academic Advising, 101 Oregon Hall, 541-346-3211 (8 a.m. to 5 p.m., Monday through Friday). If you are attempting to completely withdraw after business hours, and have difficulty, please contact the Office of Academic Advising the next business day.

Expanded Course Description
This course prepares students to apply computational, statistical, and inferential techniques to large data sets. Students will learn to obtain data from public sources, distill critical information, characterize the data using statistical techniques, and make quantitative predictions based on their analyses. Topics include the normal distribution, confidence intervals, regression, and classifiers. Ethical concerns resulting from use of the techniques in this course will be addressed.
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Release: 8.11