- Overview
- Learning Outcomes
- Requirements
- Curriculum Details
Program Purpose
The Master of Science in Statistics program aims to equip students with advanced knowledge and skills in statistical methodologies, data analysis, and mathematical modeling for applications in research, decision-making, and various industries.
Educational Objectives
The Master of Science in Statistics program aims to:
- To provide students with a comprehensive understanding of advanced statistical theories, methodologies, and mathematical foundations.
- To develop proficiency in applying statistical methods to analyze complex datasets, fostering the ability to derive meaningful insights and make informed decisions.
- To equip students with the skills and tools necessary for conducting independent research in statistics, preparing them for contributions to academia, industry, or interdisciplinary fields.
Faculty
Program Learning Outcomes
After completing this program, students should:
- To provide students with a comprehensive understanding of advanced statistical theories, methodologies, and mathematical foundations.
- To develop proficiency in applying statistical methods to analyze complex datasets, fostering the ability to derive meaningful insights and make informed decisions.
- To equip students with the skills and tools necessary for conducting independent research in statistics, preparing them for contributions to academia, industry, or interdisciplinary fields.
Admissions Requirement
Institutional-wide Admission Criteria
- Completion of undergraduate degree
- Official Transcript: allow for evaluation of academic performance, relevant coursework, and overall readiness for college-level study.
- Personal Statement: helps reviewers understand the applicant’s motivations and aspirations to pursue the program of study.
- CV: presents the academic and professional history of the applicant
- Letters of Recommendation: Letters of recommendation from teachers, mentors, or professionals familiar with the applicant’s abilities and potential, and additional insights into the applicant’s character, work ethic, and potential for success in the program.
Program-specific Criteria
- All applicants to the MS in Statistics should have an undergraduate degree in data science, statistics, computer science, applied mathematics, or another major with adequate quantitative background.
Graduation Requirement
- Successful completion of the curriculum with a grade point average of no less than B minus (2.7).
- Take at least 50% of required credits from FTC Northern.
- Pass Statistics Qualifying Examinations.
- Complete the MS degree within 4 years of matriculation to the program.
Curriculum Overview
The MS in Statistics is a 36-semester credit curriculum with two major components: core requirements and electives, plus an optional thesis. In addition, students are required to pass the Statistics Qualifying Examinations in order to graduate.
MS of Science in Statistics Curriculum
| Area | Credits |
|---|---|
| Core Requirements | 18 |
| Electives | 12 or 18 |
| Optional Thesis | 6 or 0 |
| Total Required Credits for Graduation | 36 |
Curriculum Details
| Code | Course Title | Credits | Prerequisite(s) |
|---|---|---|---|
| Core Requirements (18 cr) | |||
| STA502 | Probability Theory | 3 | None |
| STA511 | Advanced Regression Analysis | 3 | None |
| STA512 | Statistical Inference | 3 | STA502 |
| STA521 | Design and Analysis of Experiments | 3 | None |
| STA571 | Advanced Statistical Computing | 3 | None |
| STA631 | Multivariate Analysis | 3 | STA512 |
| Electives (18 cr) Complete 18 credits from the following | |||
| STA541 | Survival Analysis | 3 | STA512 |
| STA561 | Statistical Consulting | 3 | None |
| STA635 | Bayesian Statistics | 3 | STA512 |
| STA651 | Categorical Data Analysis | 3 | STA512 |
| STA671 | Linear Models | 3 | STA511 & STA512 |
| STA701 | Generalized Linear Models | 3 | STA671 |
| STA711 | Advanced Topics in Statistical Modeling | 3 | STA512 |
| STA745 | Nonparametric Statistics | 3 | STA512 |
| COS536 | Applied Machine Learning | 3 | DAS541 |
| COS541 | Big Data and Data Engineering | 3 | None |
| DAS541 | Data Mining for Business | 3 | Approval by Instructor |
| STA501 | Intermediate Statistics | 3 | None |
| STA515 | Statistical Software Programming | 3 | None |
| STA751 | Applied Statistics Project or Thesis | 6 | Dept. Approval |
| Total Credits Required for Graduation | 36 | ||
