Course Syllabus

Meeting Times and Locations

• Lecture: Monday, Wednesday and Friday 10:00 a.m. - 10:50 a.m. in Herrick 313.

Instructor

• John Paxton: Office Hours are Monday, Wednesday and Friday from 11:00 a.m. - 11:50 a.m. in Barnard Hall 353 and by appointment.

Undergraduate Course Assistant

• Henry Jacobson. Office hour: Wednesday 900-950 in Barnard 259.

Additional Help

• Alex Ellingsen. Office hour: TBD in Barnard 259.

Textbook and Resources

• Computational and Inferential Thinking: The Foundations of Data Science (free!)
• Online reference: datascience module
• Learning Data Science (free!)
• Computer Science Help Center.
• Private tutors can be arranged through SmartyCats for $2/hour.

Data Sets

• NEA Creative Writing Fellowships
• MSU Social Data

Catalog Description

• Credits: 3
• Prerequisites: CSCI 127 and M 151.
• Description: This course offers a comprehensive introduction to data science, emphasizing computational methods and statistical techniques to analyze and extract knowledge from data. It covers the entire data science pipeline, from data acquisition and cleaning to analysis, visualization, and communication of results. This course not only builds foundational skills in data science but also encourages critical thinking about ethical issues and the responsible use of data. The hands-on approach ensures that students are well-prepared for further academic pursuits or careers in data science and related fields.

Course Outcomes

By the end of this course, students should be be able to:

• To gain proficiency in data manipulation, analysis, and evaluation using computational methods.
• To learn and apply core computational and statistical techniques for data science.
• To develop skills in effective data visualization and result communication.

Graded Items

Note: Practicums must be taken at the regularly scheduled time.

• Attendance - 10% (on non-practicum days). Experience shows that students who attend class regularly tend to perform much better than students who don't. To incentivize attendance, attendance will be taken on most non-practicum days. If you attend at least 80% of the days when attendance is taken, you will earn the entire 10%. Otherwise your percent will reflect your attendance rate when attendance is taken.
• Practicum 1 - 10%
• Practicum 2 - 10%
• Practicum 3 - 20%
• Assignments - 50%

Grading Policy

Grades will be determined (after any curving takes place) based on your class average as follows:

• 93+: A
• 90+: A-
• 87+: B+
• 83+: B
• 80+: B-
• 77+: C+
• 73+: C
• 70+: C-
• 67+: D+
• 63: D
• 60: D-

If you fall within one percentage point of the next grade higher, your grade on the final practicum will be examined. If it justifies you being in the next higher grade category, you will receive that higher grade.

Collaboration Policy

All students should read the MSU Student Conduct Code.

When it comes to assignments, you should do your own work but you may

• Work with the other people on your team if teams are allowed. Each assignment will specify the maximum number of people per team.
• Share ideas with people in other teams.
• Help other teams troubleshoot problems.

Academic misconduct will result in an "F" for the course and being reported to the Dean of Students.

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Last modified: January 17, 2025.