DSCI 102 is the second course in an introductory series providing students with an understanding of fundamental concepts in data science. This course expands upon critical concepts and skills introduced in DSC 101.
In DSCI 102 students will be prepared 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.
This course is open to non-majors.
Course Syllabus:File Spring 2022 Syllabus
Spring 2021 Syllabus (Archived)
DSCI 101, Math 101 (or equivalent math placement score)