Meeting Times and Locations
- Lecture: Monday, Wednesday and Friday noon - 12:50 p.m. in
Barnard 108.
Instructor
- John Paxton:
Office Hours are Monday, Wednesday and Friday from 1:10 p.m. - 2:00 p.m.
in Barnard Hall 353 and by appointment.
Undergraduate Course Assistant
- Alex Ellingsen. Office hour: Tuesdays at 2:10 p.m. in Barnard 259.
Textbook and Resources
Data Sets
Catalog Description
- Credits: 3
- Prerequisites: CSCI 127 and M 151.
- Description: This course provides a comprehensive introduction
to data science, focusing on computational methods and statistical
techniques for analyzing and extracting insights from large datasets.
Students gain experience with the entire data science pipeline and
will be prepared for more advanced data science coursework
Course Outcomes
By the end of this course, students should be be able to:
- Formulate relevant questions from real-world data and perform exploratory data analysis using Python.
- Create and use visualizations to identify patterns, trends, and potential associations in data.
- Utilize statistical and machine learning techniques to draw inferences and make data-driven decisions.
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.
Last modified: August 22, 2025.