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Statistics and Data Science, Master of Science
This MS program (with thesis and non-thesis options) provides students with the content knowledge, critical thinking, programming, and communication skills that constitute a strong foundation in graduate-level statistics and data science. By blending statistical reasoning with advanced computational methods and data management skills, students will be well prepared to work in statistics- or data science-related fields or to pursue further education beyond the master's degree.
The program offers rigorous training options in students' areas of interest, whether in mathematically driven statistical applications or computationally driven data science skills, while expanding their knowledge and exposing them to a range of topics essential to the growing demands of statistics and data science applications.
Requirements Accordion Open
To receive a master's degree at Northern Arizona University, you must complete a planned group of courses from one or more subject areas, consisting of at least 30 units of graduate-level courses. Many master's degree programs require more than 30 units.
You must additionally complete:- All requirements for your specific academic plan(s). This may include a thesis.
- All graduate work with a cumulative grade point average of at least 3.0.
- All work toward the master's degree must be completed within six consecutive years. The six years begins with the semester and year of admission to the program.
Individual degree programs may exceed the baseline University Policy for a master's degree. The program-specific requirements are provided on the Details tab below.
Read the full policy here.
Overview Accordion Closed
In addition to University Requirements:
- Complete individual plan requirements.
| Minimum Units for Completion | 32 |
| Additional Admission Requirements | Individual program admission requirements over and above admission to NAU are required. |
| Fieldwork Experience/Internship | Optional |
| Thesis | Thesis may be required by chosen emphasis or offered as an option. |
| Oral Defense | Oral Defense may be required by chosen emphasis or offered as an option. |
| Research | Individualized research may be required by chosen emphasis or offered as an option. |
| Progression Plan Link | View Program of Study |
Purpose Statement
The MS Statistics and Data Science degree program provides students with the content knowledge, critical thinking, programming, and communication skills that constitute a strong foundation in graduate-level statistics and data science. This foundation highlights the interconnection among the different branches within the broad field of data science. By blending statistical reasoning with advanced computational methods and data management skills, students will be well prepared to work in statistics- or data science-related fields or to pursue further education beyond the master's degree.
The program offers rigorous training options in students' desired areas, whether in mathematically driven statistical applications or computationally driven data science skills, while expanding their knowledge and exposing them to a range of topics essential to the ever-growing demands of statistics and data science applications.
Student Learning Outcomes
- Demonstrate breadth and depth of knowledge of statistics and data science applications at the graduate level.
- Understand statistical theory and computational statistics, which are central to advanced studies in statistics. This foundation provides the framework for understanding and applying advanced statistical methods and the development of new statistical data science applications.
- Apply advanced statistical models and inference methods.
- Use programming to implement modern statistical and data science methods applicable to diverse situations.
- Demonstrate statistical and data science reasoning skills at the graduate level.
- Choose and implement analysis methods based on study-design constraints, data available, and scientific questions of interest.
- Manage, pre-process, and prepare data for visualizations and subsequent analyses.
- Assess the statistical significance of aspects of a proposed model and interpret the results in the situational context.
- Understand and critique new statistical methodology and its relevance to a particular study or scientific problem.
- Apply computational methodologies to implement advanced modeling techniques and statistical and machine learning methods. Graduates will have a deep conceptual understanding and the ability to detect errors in the implementation of these methods.
- Develop effective communication skills that equip them for success in industry, government service, or advanced doctoral training.
- Explain statistical methodology, assumptions, and results, both written and oral means
- Produce professional technical documents and presentations.
- Use numerical, graphical, and narrative methods for conveying analytical information.
- Communicate effectively with statisticians and data scientists, interdisciplinary researchers, and the community at large by tailoring the level of complexity and detail to the audience.
Details Accordion Closed
Graduate Admission Information
The NAU graduate online application is required for all programs. Admission to many graduate programs is on a competitive basis, and programs may have higher standards than those established by the Office of Graduate and Professional Studies.
Admission requirements include the following:- Transcripts.
- Undergraduate degree from a regionally accredited institution with a 3.0 GPA on a 4.0 scale ("A" = 4.0), or the equivalent.
Visit the NAU Graduate Admissions website for additional information about graduate school application deadlines, eligibility for study, and admissions policies.
Ready to apply? Begin your application now.International applicants have additional admission requirements. Please see the International Graduate Admissions Policy.
Additional Admission Requirements
Individual program admission requirements over and above admission to NAU are required.
- Essay/Letter of Intent/Personal Statement*
- List of courses taken in the field with titles and authors of the textbooks used.
- Prerequisite Coursework
- 23 units of undergraduate mathematics, statistics, and data science coursework at the level of calculus.
- Coursework must be completed with a grade of "C" or better and have a 3.0 GPA or higher.
- The 23 units must include coursework in the following:
- Calculus-based Probability Distributions
- Intermediate Calculus
- Linear Algebra
- Programming
- The 23 units must included at least one of the following:
- Discrete Mathematics
- Mathematical Statistics
- Multivariable Calculus
- Real Analysis
- 23 units of undergraduate mathematics, statistics, and data science coursework at the level of calculus.
- Recommendation(s)/Reference(s)*
*See the application for details.
Master's Requirements
This Master's degree requires 32 units distributed as follows:
- Required Coursework: 14 units
- Electives: 12 units
- Additional Coursework or Thesis Requirement - Select one: 6 units
Take the following 32 units:
- Students completing a non-thesis, coursework, project, track, internship, track, or exam option must complete 24 units of formal letter-graded coursework.
- Students completing a thesis are required to complete 18 units of formal letter-graded coursework.
Electives (12 units)
- Select from the following (12 units):
The following courses have additional prerequisites:
Additional Coursework or Thesis Requirement - Select one (6 units)
Additional Coursework Option (6 units)
- Select additional from the following (6 units):
The following courses have additional prerequisites:
Thesis Option (6 units)
- STA 699 - for the research, writing and oral defense of an approved thesis. (6 units)
Additional Information
Some courses may have prerequisites. For prerequisite information, click on the course or see your advisor.
Master of Science in Statistics
With this degree, you will be trained in both the theory and application of statistics in a specific mathematics related area of your choice. Examples of these areas could be in:- actuarial science
- applied mathematics
- applied statistics
- environmental science
- forestry