Course Description

This course introduces students to fundamental statistical methods and tools used in data science to produce, analyze, and communicate about data. Topics will include measures of center and spread, data visualizations, hypothesis testing, confidence intervals, linear regression, and others as time allows. The course emphasizes conceptual understanding and written, oral, and visual communication while de-emphasizing memorization, algebraic manipulation, and by-hand calculation. The course will employ Python code to implement methods and analyze data. There are no formal prerequisites for the course, but comfort with college algebra and some coding experience will be a plus.

Divisional Requirements

Was course completed satisfactorily?

Yes

Course Fundamentals

Attended class
Participated in class discussion
Participated in in-class or group activities
Completed assignments on time

Learning Goals

Learn to read and interpret intellectual or artistic works
Write critically and analytically
Understand quantitative methods of analysis
Develop creative abilities in expressive modes (e.g. creative writing, visual and performance arts, and music)
Effectively present ideas orally
Conceive and complete project-based work
Understand multiple cultural perspectives on intellectual or artistic subjects

Narrative Description of Student Performance

Overall, Ruth Daniel was a consistently engaged student who demonstrated an excellent understanding of most of the course material. The course content was organized into a total of 34 specific course objectives that students were expected to master by the end of the semester, and students could take as many attempts as necessary to master each one. Students were also required to complete a final project. Ruth was among the top students in the class, mastering 32 of the 34 available course objectives. Ruth’s homework performance was exemplary both in terms of a high completion rate and a high accuracy rate. In class, Ruth worked diligently on the class activities with patience and persistence. They asked good questions, and their positive attitude was an asset to the class.

For the final project, students were to use techniques from the course to study some attribute of interest involving the Hampshire campus. Ruth analyzed student traffic at the Kern Cafe on campus. They were careful about defining their variables, and their presentation was well-organized and clear. While they used appropriate statistical techniques that were executed correctly, and while they generated useful results, as Ruth acknowledges in their paper, their results would have been strengthened by a larger sample size.