Data Science (DSCI) |
204 Pacific Hall,
College of Arts & Sciences
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Course Data
| DSCI 102 + Lab |
0.00 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
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| Instructor: |
Diaz Monzon M |
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| Course Materials |
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CRN |
Avail |
Max |
Time |
Day |
Location |
Instructor |
Notes |
+ Lab |
31401 |
17 |
25 |
0900-0950 |
f |
193 ANS |
Diaz Monzon M |
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Associated Sections |
Lecture |
31399 |
44 |
100 |
1600-1720 |
tr |
129 MCK |
Sventek J |
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Academic Deadlines
| Deadline |
Last day to: |
| March 31: |
Process a complete drop (100% refund, no W recorded) |
| April 6: |
Drop this course (100% refund, no W recorded; after this date, W's are recorded) |
| April 6: |
Process a complete drop (90% refund, no W recorded; after this date, W's are recorded) |
| April 7: |
Process a complete withdrawal (90% refund, W recorded) |
| April 7: |
Withdraw from this course (100% refund, W recorded) |
| April 8: |
Add this course |
| April 8: |
Last day to change to or from audit |
| April 14: |
Process a complete withdrawal (75% refund, W recorded) |
| April 14: |
Withdraw from this course (75% refund, W recorded) |
| April 21: |
Process a complete withdrawal (50% refund, W recorded) |
| April 21: |
Withdraw from this course (50% refund, W recorded) |
| April 28: |
Process a complete withdrawal (25% refund, W recorded) |
| April 28: |
Withdraw from this course (25% refund, W recorded) |
| May 19: |
Withdraw from this course (0% refund, W recorded) |
 | 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. |
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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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