Meeting Times and Locations

• Lecture: Monday, Wednesday and Friday 9:00 a.m. - 9:50 a.m. in Barnard Hall 103.
• Optional Open Lab: Tuesday 8:00 a.m. - 10:50 a.m. and 5:10 p.m. - 6:00 p.m. in Roberts 111. Open to all 127 students.
• Regular lab sections: Sections 001 through 006
• Honor's lab section: 008

Instructor

• John Paxton: Office Hours are Monday, Wednesday, Friday from 10:00 a.m. - 10:50 a.m. in Barnard Hall 353 and by appointment (via e-mail).
• Ann Marie Reinhold: Office Hours are Wednesday 10:00 a.m. - noon and Friday 10:00 a.m. - 10:50 a.m. in Barnard Hall 356 and by appointment.

Teaching Assistants

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

• 11:00 a.m. lab (section 001): Sultan Yarylgassimov. Office hour: Thursdays 1000-1200
• 12:00 p.m. lab (section 002): Sultan Yarylgassimov. Office hour: Thursdays 1000-1200 .
• 1:10 p.m. lab (section 003): Gage Nesbit. Office hour: Fridays 1510 - 1600 .
• 2:10 p.m. lab (section 004): Brady Ash. Office hour: Tuesdays 1310 - 1400.
• 3:10 p.m. lab (section 005): Connor Meyn. Office hour: Tuesdays 1100 - 1150 .
• 4:10 p.m. lab (section 006): Kaden Bach. Office hour: Tuesdays 900 - 950 .
• 6:10 p.m. lab (section 008): Maksym Makarchuk. Office hour: Tuesdays 1200 - 1250.

Tuesday Open Lab Staffing

• 8:00 a.m. - Kaden Bach
• 9:00 a.m. - Brady Ash, Sultan Yarylgassimov
• 10:00 a.m. - Gage Nesbit, Sultan Yarylgassimov
• 5:10 p.m. - Connor Meyn

Additional Lab Helpers (CSCI 495 Students)

• 800-950: Man Ho Yuen
• 900-1050: Cole Reimer
• 1100-1300: Jared Matury, Rory Schillo
• 1200-1400: Aurora Duskin, Gage Hilyard
• 1310-1500: Elyse Dalager
• 1410-1600: Bryce Leighton
• 1510-1700: Connor Parrot

Textbook and Resources

• The online textbook is free.
• Python 3.11 and IDLE editor - download site.
• Python installation instructions for Mac and Windows. Both platforms: the current version is 3.11. Windows: it is important to select the "add python.exe to path" option.
• 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.
• Shane Costello (shanecost2002@gmail.com) is a SmartyCats tutor for CSCI 127. Shane has drop-in hours every Wednesday at the MSU Library from 5 p.m. - 7 p.m. as well as available appointments on Tuesdays and Thursdays.

Catalog Description

• Credits: 4
• 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.

Inclusivity Statement

We support an inclusive learning environment where diversity and individual differences are understood, respected, appreciated, and recognized as a source of strength. We expect that everyone in this class will respect differences and demonstrate diligence in understanding how other peoples' perspectives, behaviors, and worldviews may be different from their own.

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Last modified: November 6, 2023.