Meeting Times and Locations
- Lecture: Monday, Wednesday and Friday 9:00 a.m. - 9:50 a.m. in
Leon Johnson 339.
- 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.
- 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.
Note: All labs take place on Tuesday in Roberts 111 and all
office hours are held in Barnard Hall 259.
- 8:00 a.m. lab (section 001): Xuying Wang. Office Hour: Monday 8:00 a.m. - 8:50 a.m..
- 10:00 a.m. lab (section 002): Garret Gershmel. Office Hour: Tuesday noon - 1:00 p.m.
- 12:15 p.m. lab (section 003): Trent Baker. Office Hour: Wednesday 3:10 p.m. - 4:00 p.m..
- 2:15 p.m. lab (section 004): Marie Morin. Office Hour: Thursday 4:10 p.m. - 5:00 p.m..
- 4:15 p.m. lab (section 005): Justin McGowen. Office Hour: Tuesday 11:00 a.m. - noon.
- 6:15 p.m. lab (section 006): Hugh O'Neill. Office Hour: Thursday 5:00 p.m. - 6:00 p.m..
Tuesday Lab Helpers
- 8:00 a.m. lab (section 001): Will Brensdal?, Ethan Peterson
- 10:00 a.m. lab (section 002): Ryan Brand, Madison Fichter.
- 12:15 p.m. lab (section 003): Daniel Church, Matthew Nitschke.
- 2:15 p.m. lab (section 004): Emily Felde, Jordan Sparr.
- 4:15 p.m. lab (section 005): Courtney Linder, Connor Overcast.
- 6:15 p.m. lab (section 006): Alex Bauer.
Textbook and Resources
- 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.
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.
- Virgina Tech's Collection of Really Great, Interesting, Situated
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)
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.
All students should read the
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.
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),
veterans 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: January 17, 2018.