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 11:
Drop this course (100% refund, no W recorded; after this date, W's are recorded)
January 11:
Process a complete drop (90% refund, no W recorded; after this date, W's are recorded)
January 12:
Process a complete withdrawal (90% refund, W recorded)
January 12:
Withdraw from this course (100% refund, W recorded)
January 13:
Add this course
January 13:
Last day to change to or from audit
January 19:
Process a complete withdrawal (75% refund, W recorded)
January 19:
Withdraw from this course (75% refund, W recorded)
January 26:
Process a complete withdrawal (50% refund, W recorded)
January 26:
Withdraw from this course (50% refund, W recorded)
February 2:
Process a complete withdrawal (25% refund, W recorded)
February 2:
Withdraw from this course (25% refund, W recorded)
February 23:
Withdraw from this course (0% refund, W recorded)
February 23:
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.
