Computational Statistics and Data Science (BS)

Computational Statistics and Data Science (BS)

• Program Prerequisite: Not required for Computational Statistics and Data Science.
• Minor: No minor required
• Grade Requirements: A grade of “C” or better in courses required for this major (a grade of “C-” is not acceptable), in addition to an overall 2.0 GPA and a 2.0 GPA in mathematics courses numbered 1210 or above.
• Credit Hour Requirements: A minimum total of 120 credit hours are required for graduation; 48 of these are required within the major. A total of 40 upper division credit hours are required (courses numbered 3000 and above); at least nine (9) credit hours of upper division Mathematics must be completed at Weber State University.
• Graduate Credit Restriction: Undergraduate students have a 6-credit max of credit-share for 6000-level courses.
• Program Code: 6037BS
• CIPC: 27.9999

Advisement

All Mathematics majors should see the Mathematics Department to be assigned an advisor. They should meet with their advisors at least once a year to help plan their programs and check on their progress. Call 801-626-6095 for more information or go to mathadvising.youcanbook.me to schedule an appointment. (Also refer to the Department Advisor Referral List.)

Use Grad MAPs to plan your degree.

Admission Requirements

Declare your program of study (see Program of Study (Major/Minor) Declaration) with your major department. There are no special admission or application requirements for the Regular Mathematics, Applied Mathematics, or Computational Statistics and Data Science majors. Mathematics Teaching majors must meet the Teacher Education admission and licensure requirements (See Teacher Education Department).

General Education

Refer to Degree Requirements for either Bachelor of Science or Bachelor of Arts requirements. COMM 1020  (HU), and COMM 2110  (HU) will fulfill requirements for both the major and general education.

Program Learning Outcomes

• Students will understand the theoretical, conceptual, and applied underpinnings of Statistics
• Students will understand the theoretical, conceptual, and applied underpinnings of Data Science
• Students will demonstrate fundamentals and fluency in computation.
• Students will effectively analyze and reason with data.
• Students will be able to effectively communicate their results