Meeting Times and Locations

• Lecture: Monday, Wednesday and Friday 9:00 a.m. - 9:50 a.m. in NAH 165.
• Laboratory: Thursday 8:00 a.m. - 9:50 a.m. (section 1), 10:00 a.m. - 11:50 a.m. (section 2), 12:15 p.m. - 2:05 p.m. (section 3), 2:15 p.m. - 4:05 p.m. (section 4), 4:15 p.m. - 6:05 p.m. (section 5), 6:15 p.m. - 8:05 p.m. (section 6) in Roberts 111.

Instructor

• John Paxton: Office Hours are Monday, Wednesday and Friday from 10:00 a.m. - 10:50 a.m. in Barnard Hall 353 and by appointment.

Teaching Assistants

Note: All labs take place on Thursday in Roberts 111 and all office hours are held in Barnard Hall 259.

• 8:00 a.m. lab (section 001): Peter Gifford. Office Hour: Wednesday 1:10 p.m. - 2:00 p.m.
• 10:00 a.m. lab (section 002): Madison Fichtner. Office Hour: Wednesday 11:00 a.m. - 11:50 a.m.
• 12:15 p.m. lab (section 003): Eric Kempf. Office Hour: Thursday 4:30 p.m. - 5:30 p.m.
• 2:15 p.m. lab (section 004): Jessica Jorgenson. Office Hour: Thursday 4:10 p.m. - 5:00 p.m.
• 4:15 p.m. lab (section 005): Reese Pearsall. Office Hour: Thursday 1:10 p.m. - 2:50 p.m.
• 6:15 p.m. lab (section 006): Cesar Cruz. Office Hour: Thursday noon - 1:00 p.m.

Thursday Lab Helpers

• 8:00 a.m. lab (section 001): William Kingsley, Jace Rost
• 10:00 a.m. lab (section 002): John Bemis, Logan Davis
• 12:15 p.m. lab (section 003): Benjamin Bushnell, Madison Fichtner
• 2:15 p.m. lab (section 004): Kyle Melton, Jazzlyn Pulley
• 4:15 p.m. lab (section 005): Spencer Cornish, Brandon Klise
• 6:15 p.m. lab (section 006): Carsen Ball, Henry Barker

Textbook and Resources

• The online textbook is free.
• Python 3.6.7 and IDLE editor - download site.
• Python module installation instructions.
• Computer Science Help Center.
• SmartyCats offers free CSCI 127 drop-in tutoring sessions at the library each Saturday from 10:00 a.m. to 1:00 p.m. and from 3:00 p.m. to 5:00 p.m. Private tutors can also be scheduled for $2/hour.

Catalog Description

• Credits: 4
• Corequisite: M 151Q
• Recommended Prerequisite: Prior programming experience OR CSCI 107
• Description: Provides a gentle introduction to the exciting world of big data and data science. Students expand their ability to solve problems with Python by learning to deploy lists, files, dictionaries and object-oriented programming. Data science libraries are introduced that enable data to be manipulated and displayed.

Course Outcomes

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

• Utilize lists, files, dictionaries and arrays to solve problems in Python.
• Utilize fundamental object oriented principles such as classes, objects, methods and inheritance to solve problems in Python.
• Utilize data science libraries to solve data science problems in Python.
• Understand the broad area of data science and its relevance.

Additional Datasets

• Virgina Tech's Collection of Really Great, Interesting, Situated Datasets
• World Health Organization Tuberculosis Dataset.

Graded Items

Note: Practicums must be taken at the regularly scheduled time and will not be given early.

• Practicum 1 - 15%
• Practicum 2 - 15%
• Practicum 3 - 25%
• In Labs - 15% (all weighted equally)
• Programming Assignments - 30% (all weighted equally)

Grading Policy

To pass the course, you must average at least 50% on the practicums. Assuming that this is the case, 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 exam 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 Python assignments, 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.

You may NOT

• Share code you write with other teams.
• Submit code that someone on your team did not write.
• Modify another team's solution and claim it as your own.

Failure to abide by these rules will result in an "F" for the course and being reported to the Dean of Students.

Diversity Statement

Montana State University's campuses are committed to providing an environment that emphasizes the dignity and worth of every member of its community and that is free from harassment and discrimination based upon race, color, religion, national origin, creed, service in the uniformed services (as defined in state and federal law), veteran’s status, sex, age, political ideas, marital or family status, pregnancy, physical or mental disability, genetic information, gender identity, gender expression, or sexual orientation. Such an environment is necessary to a healthy learning, working, and living atmosphere because discrimination and harassment undermine human dignity and the positive connection among all people at our University. Acts of discrimination, harassment, sexual misconduct, dating violence, domestic violence, stalking, and retaliation will be addressed consistent with this policy.

---

Last modified: February 13, 2019.