• This worksheet accompanies the 2024-2025 Catalog
  • Last Updated: 10/3/2024
  • It is recommended that you utilize this worksheet in combination with your DegreeWorks worksheet.
  • Keep this worksheet up to date and bring it with you each time you meet with your advisor.  It will help your advisor give you better advice when you register for classes.
  • Substitute courses are sometimes allowed and must be approved by the GSOC.  See your advisor for specific questions.   Do not assume that a substitution will be allowed.
  • You may not use pass/fail courses except in the unrestricted electives section (with the exception of Spring 2020 COVID-19 courses that were converted to pass).
  • All course grades must be at least a C- to count towards your degree. 
  • Check out this typical semester-by-semester schedule of classes.
  • This is a complex form and may contain a few errors.  If you notice any, please notify Hunter Lloyd (hunter.lloyd@montana.edu) or Shane Blanchard (shaniah.blanchard@montana.edu).

 Section 1.  Required Business Courses

  • BMIS 211, Data Analytics I, 3 credits
  • BMIS 312, Data Analytics II, 3 credits

 

Total Credits:

300+ Level Credits:

 Section 2.  Required Computer Science Courses

  • CSCI 127, Joy and Beauty of Data, 4 credits
  • CSCI 132, Basic Data Structures and Algorithms, 4 credits
  • CSCI 232, Data Structures and Algorithms, 4 credits
  • CSCI 246, Discrete Structures, 3 credits (or M 242, Methods of Proof, 3 credits)
  • CSCI 347, Data Mining, 3 credits
  • CSCI 440, Database Systems, 3 credits
  • CSCI 447, Machine Learning, 3 credits
  • CSCI 491-003 Data Science Capstone, 3 credits

 

Total Credits:

300+ Level Credits:

 Section 3.  Required Library Science Course

  • LSCI 470, Ethics and Privacy in the Age of Big Data, 3 credits

 

Total Credits:

300+ Level Credits:

 Section 4.  Required Math and Statistics Courses

  • M 171, Calculus I, 4 credits
  • M 172, Calculus II, 4 credits
  • M 221, Introduction to Linear Algebra, 3 credits
  • M 273, Multivariable Calculus, 4 credits
  • STAT 216, Introduction to Statistics, 3 credits
  • STAT 337, Intermediate Statistics with Introduction to Statistical Computing, 3 credits
  • STAT 411, Methods for Data Analysis I, 3 credits
  • STAT 412, Methods for Data Analysis II, 3 credits

 

Total Credits:

300+ Level Credits:

 Section 5.  Data Science Electives

  • You need 12 credits from this section.
  • Other upper division data intenive courses if pre-approved can be used.

Courses that fulfill this requirement:

  • BMGT 405, Supply Chain Analytics, 3 credits (BMGT 322 or EIND 458 pre-req)
  • BMGT 475R, Management Research Experience, 3 credits (BMGT 335 pre-req)
  • CSCI 432, Advanced Algorithm Topics, 3 credits
  • CSCI 446, Artificial Intelligence, 3 credits
  • CSCI 451, Computational Biology, 3 credits
  • ESOF 322, Software Engineering, 3 credits
  • LSCI 342, Data Curation for a Data Driven World, 3 credits
  • LSCI 437, Social Media Practices, 3 credits
  • M 274, Differential Equations, 4 credits
  • M 348, Techniques of Applied Math I, 3 credits
  • M 349, Techniques of Applied Math II, 3 credits
  • M 386R, Software Applications in Mathematics, 3 credits
  • M 430, Mathematical Biology, 3 credits
  • M 441, Numerical Linear Algebra & Optimization, 3 credits
  • M 442, Numerical Solutions of Differential Equations, 3 credits
  • STAT 408, Statistical Computing and Graphical Analysis, 3 credits (recommended)
  • STAT 425, Biostatistical Data Analysis, 3 credits
  • STAT 439, Introduction to Categorical Data Analysis, 3 credits
  • STAT 441, Experimental Design, 3 credits
  • STAT 446, Sampling, 3 credits
  • ECNS 403, Intro to Econometrics, 3 credits (ECNS 301 pre-req)
  • EFIN 301, Engineering & Economic Financial Management I, 3 credits
    • ECNS 301-preq
    • STAT 216/217 or STAT 332 can substitute for EIND 354 pre-req
  • EFIN 305, R Lab for Financial Engineering I, 1 credit (EFIN 301 co-req)
  • EFIN 401, Engineering & Economic Financial Management II, 3 credits (EFIN 301 pre-req)
  • EFIN 405, R Lab for Financial Engineering II, 1 credit (EFIN 401 co-req)

 

Total Credits:

300+ Level Credits:

Section 6.  Free Electives

  • You need 12 credits from this section.
  • This is intended to be a focus that you wish to pair with Data Science.

 

  • Course 1:
  • Course 2:
  • Course 3:
  • Course 4:
  • Course 5:
  • Course 6:
  • Etc.

 

Total Credits:

300+ Level Credits:

Section 7.  Core 2.0/Computer Science Accreditation Core

  • US Core, 3 credits:
  • WRIT 101 W, College Writing I, 3 credits
  • WRIT 221, Intermediate Technical Writing, 3 credits
  • IA or RA Core, 3 credits:
  • IH or RH Core, 3 credits:
  • IS or RS Core, 3 credits:
  • D Core, 3 credits:
  • CS Core, 3 credits:
  • IN Core, 3 credits:

 

Total Credits:

300+ Level Credits:

 Section 8.  Unrestricted Electives

  • You might need to take additional credits of elective courses to bring your credit total to 120.  Add up the credit totals in the other sections and subtract from 120 to determine exactly how many unrestricted elective credits you need.
  • Any university course may be used in this section if it has not been used in another section on this worksheet.
  • You must accumulate at least 42 credits in courses numbered 300 or above.  If you have taken courses at this level that are not listed in any other section on this worksheet, and if you need to count these courses in order to meet the 42 credit requirement, list them here.

Courses used to fuilfill these requirements:

  • Course 1 (if needed):
  • Course 2 (if needed):
  • Course 3 (if needed):
  • Course 4 (if needed):
  • Etc.

 

Total Credits:

300+ Level Credits:

Section 9.  Checklist for Graduation

Total number of credits (must be at least 120):

Total number of 300+ level credits (must be at least 42):

 All course grades are at least a C- (yes or no):

 Section 10.  Graduation Application Instructions

Congratulations – you are almost finished!  Graduation Applications must be submitted by the following dates:

  • October 1st for Spring Graduation.
  • March 1st for Summer or Fall Graduation.

 Instructions:

  1. Create your Final Semester Plan in DegreeWorks.
  2. Make an appointment to meet with your assigned faculty advisor.
  3. If the requirements of the major are met through your Final Semester Plan, your advisor will lock the plan and enter the note "Final Semester Primary Degree Advisor Approves of DegreeWorks Worksheet" into DegreeWorks.
  4. After this note appears, you must e-mail the following information to soc-info@montana.edu with the subject Graduation_Semester Graduating_First Name (Graduation F27 Rooney) :
    1. First & Last Name
    2. Last four digits of GID
    3. Preferred Email Address
    4. Advisor
    5. Major/Minor
  5. The GSOC office will record this information and notify our certifying officer, Hunter Lloyd.
  6. Once your major is certified, another note will be added to DegreeWorks "Final Semester Primary Degree Certified". Once that happens, you will be able to complete your graduation application in DegreeWorks.
  7. Hunter will send out an email letting the student know they are certified, or informing them of any information they may need. 
  8. After receving Hunter's email, navigate to: 1) My Info, 2) Student Services, 3) Apply to Graduate. Finish and submit the application, and voila -done!

**Please note you are required to certify your minor with the advisor of your minor department. Meet with them and follow their instructions.

Further details can be found at: https://www.montana.edu/registrar/Graduation.html.