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QMSS Part-Time Data Science Concentration
Semester Planning Worksheet, AY 2014-2015
Name: ___________________________________
Entered Program: _________________________
UNI: _________________________________
Expected Graduation: ___________________
Part-time students are expected to complete the program in 4-5 semesters of coursework.
If you want to spend additional time at Columbia, please contact QMSS program staff to discuss your academic plan.
There is more flexibility in how you can distribute your electives than this worksheet shows. Please contact the
program coordinator if you have any questions.
Thirty (30) points minimum are required for the degree. Students must maintain a 3.0 or above average.
QMSS Core Requirements
A. Theory and Methodology (QMSS G4010)
B. Two Seminars (QMSS G4021 and QMSS G4022)
C. Data Analysis Requirement to be fulfilled of the following:
 Probability and Statistics (STAT W4700)
 Multivariate Political Analysis (POLS W4912)
D. Two Additional Electives
E. Master’s Thesis (QMSS G5999)
Data Science Concentration Requirements:
F. Algorithms for Data Science (CSOR W4246)
G. Exploratory Data Analysis and Visualization (STAT W4701 or QMSS G4063)
H. Machine Learning for Data Science (COMS W4721)
Course Planner
First Year Fall Semester
Course Title
Theory and Methodology
Algorithms for Data Science
Data Analysis
First Year Spring Semester
Course Title
Machine Learning for Data Science
Exploratory Data Analysis and
Visualization
Elective Course
Second Year Fall Semester
Course Title
QMSS Seminar
Algorithms for Data Science
Second Year Spring Semester
Course Title
QMSS Seminar
Master’s Thesis
Course Number
QMSS G4010
CSOR W4246
STAT W4700
Credits
4
3
3
Semester
Fall
Fall
Spring
Year
Course Number
COMS W4721
Credits
3
Semester
Fall
Year
3 or4
Spring
Spring
Credits
2
3
Semester
Fall
Fall
Year
Semester
Spring
Spring
Year
STAT W4701 or QMSS G4063
Course Number
QMSS G4021
CSOR W4246
Course Number
QMSS G4022
QMSS G5999
Credits
2
3 or 4
Total credits (30 min)
Thesis Title: ______________________________________________________________________________________
Advisor (if applicable): _____________________________________________________________________________