Introduction to Data Science
(Same as Electrical and Computer Engineering M148.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 31 or Program in Computing 10A, and 10B, and one course from Civil and Environmental Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. How to analyze data arising in real world so as to understand corresponding phenomenon. Covers topics in machine learning, data analytics, and statistical modeling classically employed for prediction. Comprehensive, hands-on overview of data science domain by blending theoretical and practical instruction. Data science lifecycle: data selection and cleaning, feature engineering, model selection, and prediction methodologies. Letter grading.
Review Summary
- Clarity
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8.3 / 10
- Organization
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10.0 / 10
- Time
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10-15 hrs/week
- Overall
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8.3 / 10
Enrollment Progress
Enrollment data not available.
Reviews
The class is a bit more handwavy with the analysis of different algorithms, which is why I did not like it as much as M146. But this is not really Dolecek's fault, I think it's just the way that the course was designed.
Overall, Dolecek had informative slides and pretty straightforward tests, which were curved. Her lectures were pretty good, although sometimes slow to make sure everyone is on the same page, but she does record them.
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Course
Grading Information
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No group projects
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Attendance not required
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2 midterms
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10th week final
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0% recommend the textbook
Previous Grades
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