2024-2025 Undergraduate & Graduate Catalog
Combined Bachelor of Arts or Bachelor of Science in Statistics and Master of Science in Data Science and Analytics
Grand Valley State University combined degree programs (see: 961381.com/gs/combined-degree-programs-85.htm) offer students the opportunity to complete both an undergraduate and graduate degree in less time and at a lower cost. Qualified undergraduate statistics students may be admitted to the program and obtain the B.S. or B.A. in statistics and an M.S. in data science and analytics (DSA) within an accelerated time frame.
Students graduating with a combined degree in statistics and data science and analytics will
- enhance their B.S. degree in statistics;
- possess a strong knowledge of statistical applications, expertise in mathematics and statistical theory, advanced computational skills, and the deep thinking and well-rounded education earned through the undergraduate degree from a liberal arts institution;
- learn advanced statistical and computational techniques offering the potential for an accelerated career trajectory; and
- obtain valuable internship experience in the fourth year applying theory to practice.
Application Procedure
Application requirements include:
- Overall GPA of 3.25 or greater
- 60 hours of academic credit have been completed or are in progress.
- STA 216, one 300 level statistics course, and CIS 161 or CIS 162 should be completed with grades of B or better.
- Two letters of recommendation.
- Academic transcripts (unofficial transcripts are allowable)
- Letter of intent
Admission decisions will be made by the data science and analytics admissions committee based on the student's previous academic success in statistics and CIS courses, as indicated by GPA and grades in courses relevant to data science (such as STA 216 and CIS 161), as well as potential success in the graduate program, as indicated by the letters of recommendation, and the student's letter of intent. Decisions will normally be communicated to students within four weeks of submitting a complete application to the combined degree program.
Requirements During Undergraduate Studies
All university requirements, including general education courses, must be completed before the final (graduate) year of the combined degree program. In the final undergraduate year, students will normally take 12 credits of graduate-level courses. If any courses are dual-listed, students in the combined program must complete all assignments expected of graduate students and they will be evaluated in the same way as graduate students.
The school has identified the following courses that students may dual count toward the bachelor's and M.S. degrees. Up to 12 credits can be dual-counted. Students are strongly encouraged to work with the graduate program director in DSA and their statistics department advisor to ensure all undergraduate and graduate requirements are met.
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- One of STA 518 or STA 526 as both a DSA requirement and a statistics elective.
- One of STA 518 or STA 526, not previously used, as both a DSA requirement and a free undergraduate elective.
- CIS 635 as both a DSA requirement and a statistics application cognate
- CIS 660 as both a DSA requirement and a statistics application cognate
Note that either CIS 161 and CIS 500 (preferred) or CIS 162 and CIS 163 should be taken as part of the undergraduate curriculum to be prepared for the graduate CIS courses.
Requirements During Graduate Studies
Tuition levels for students will be determined by current university policy.
Graduation Without Completion of the Program
If a student decides at some point to pursue only the undergraduate portion of the combined degree, the school will still recognize the graduate courses taken in lieu of undergraduate courses. Credit from the undergraduate degree cannot be used toward a graduate degree at a later date.
Sample Curriculum Sequence
This sample order of coursework assumes that students will complete the general education courses with the help of their advisor. The following course sequence also assumes a strong mathematics background for the entering student. If mathematics deficiencies exist, completing the mathematics prerequisites should be the student's top priority. This is only one of many possible sequences of courses. Students are strongly encouraged to work with the graduate program director in DSA and their advisor in statistics to ensure all undergraduate and graduate requirements are met, and to customize the combined program to their areas of interest.
Suggested Order of Coursework
This sample order of coursework assumes that students will complete the CS foundation and general education courses with the help of their advisor and apply for undergraduate admission at the end of the winter semester of their second year. The following course sequence also assumes a strong mathematics background for the entering student. If mathematics deficiencies exist, completing the mathematics prerequisites should be the student's top priority.
Note: This is only one of many possible sequences of courses. Students are strongly encouraged to work with the graduate program director in DSA to ensure all undergraduate and graduate requirements are met, and to customize the combined program to their areas of interest. The following sequence makes no attempt to minimize credits. For example, the sequence assumes that all general education courses are distinct, and no "double dipping" is done.
Year One
Fall
- CIS 161 - Computing for Data Science Applications I (3 credits)
- MTH 201 - Calculus I (4 credits)
- General education course
- General education course
- General education course
Winter
- MTH 202 - Calculus II (4 credits)
- STA 312 - Probability and Statistics (3 credits)
- General education course
- General education course
- General education course
Year Two
Fall
- MTH 204 - Linear Algebra I (3 credits)
- STA 216 - Intermediate Applied Statistics (3 credits)
- General education course
- General education course
- General education course
Winter
- STA 311 - Introduction to Survey Sampling (3 credits) OR STA 315 - Design of Experiments (3 credits)
- STA 321 - Applied Regression Analysis (3 credits)
- Statistics Elective (300 level)
- General education course
- General education course
Year Three
Fall
- CIS 500 - Fundamentals of Software Practice (3 credits)
- STA 419 - Statistics Project (3 credits)
- STA 430 - History of Statistics (1 credit)
- STA 518 - Statistical Computing and Graphics with R (3 credits)
- General education course
- General education course
Winter
- CIS 635 - Knowledge Discovery and Data Mining (3 credits)
- STA 526 - Multivariate Data Analysis (3 credits)
- General education course
- General education course
- General education course
Year Four
Fall
- CIS 660 - Data Engineering (3 credits)
- STA 412 - Mathematical Statistics I (4 credits)
- General education course
- General education course
- General education course
Winter
- CIS 678 - Machine Learning (3 credits)
- STA 415 - Mathematical Statistics II (Capstone) (4 credits)
- General education course
- General education course
Spring/Summer
Year Five
Fall
- CIS 677 - High-performance Computing (3 credits)
- PSM 650 - Ethics and Professionalism in Applied Science (3 credits)
- STA 631 - Statistical Modeling I (3 credits)
Winter
- CIS 677 - High-performance Computing (3 credits)
- PSM 662 - Seminar in Professional Science Practice (2 credits)
- STA 616 - Statistical Programming (3 credits)
Undergraduate credits that count toward B.A./B.S. |
108 |
Graduate credits that count toward B.A./B.S. and M.S. |
12 |
Graduate credits that count toward M.S. |
24 |
Total credits |
143 |