This course utilizes a quantitative approach to explore fundamental concepts in data science. Students will develop key skills in programming and statistical inference as they interact with real-world data sets across a variety of domains. Ethical and privacy concerns are explored. Sequence with DSCI 102.
Grading Options:
Optional; see degree guide or catalog for degree requirements
Process a complete drop (100% refund, no W recorded)
January 13:
Drop this course (100% refund, no W recorded; after this date, W's are recorded)
January 13:
Process a complete drop (90% refund, no W recorded; after this date, W's are recorded)
January 14:
Process a complete withdrawal (90% refund, W recorded)
January 14:
Withdraw from this course (100% refund, W recorded)
January 15:
Add this course
January 15:
Last day to change to or from audit
January 21:
Process a complete withdrawal (75% refund, W recorded)
January 21:
Withdraw from this course (75% refund, W recorded)
January 28:
Process a complete withdrawal (50% refund, W recorded)
January 28:
Withdraw from this course (50% refund, W recorded)
February 4:
Process a complete withdrawal (25% refund, W recorded)
February 4:
Withdraw from this course (25% refund, W recorded)
February 25:
Withdraw from this course (0% refund, W recorded)
February 25:
Change grading option for this course
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 utilizes a quantitative approach to explore fundamental concepts in data science. Students will develop key skills in programming and statistical inference as they interact with real-world data sets across a variety of domains. Ethical ramifications of data collection, data-driven decision making, and privacy will be explored. This course is intended to be accessible to students without prior experience in computer programming or statistics.
