Master of Science in Statistics

Curriculum

To complement foundational courses, we offer a selection of classes that reflect the varied research interests of our growing faculty. Their areas of expertise include computational methods, Bayesian statistics, non-parametric statistics, precision medicine, machine learning, network data, survival analysis, latent class modeling, big data analysis, missing data analysis, and methods for space-time data. In order to facilitate part-time study, the department regularly offers graduate courses in late afternoons, making our program an attractive choice for people with a full-time job. Students are exposed in every class to computational methods, using extensively software like R and SAS, and have the opportunity to complete team-based course projects involving analysis of real datasets. Our classes are often attended by graduate students from other departments that bring different research experiences, helping to showcase the many areas in which statistical methods are used and are key for scientific advancement.

Thesis Option requirements

The thesis option is particularly recommended to those students who intend to follow a career path in research, either in academic, governmental or private institutions. In addition to the master’s thesis, students will complete a minimum of 30 credits as follows:

  1. At least nine credits (3 courses) selected from the following required courses:
    • MTH 451 – Introduction to Probability and Statistics (3cr)
    • MTH 452 – Mathematical Statistics (3cr)
    • At least one of the following courses:
      • STA 501 – Analysis of Variance and Variance Components (3cr)
      • STA 502 – Applied Regression Analysis (3cr)
      • STA 576 – Econometrics (3cr)
  2. At least nine additional credits selected from (3 courses):
    • STA 500 – Analysis of Missing Data (4cr)
    • STA 501 – Analysis of Variance and Variance Components (3cr)
    • STA 502 – Applied Regression Analysis (3cr)
    • STA 515 – Spatial Data Analysis (3cr)
    • STA 525 – Programming and Data Management in SAS (4cr)
    • STA 536 – Applied Longitudinal Analysis (3cr)
    • STA 541 – Multivariate Statistical Methods (3cr)
    • STA 542 – Categorical Data Analysis Methods (3cr)
    • STA 545 – Bayesian Statistics (3cr)
    • STA 550 – Ecological Statistics (3cr)
    • STA 560 – Time Series Analysis (4cr)
    • STA 585 – Statistical Analysis of Network Data (4cr)
    • STA 592 – Special Topics in Statistics. Current courses include:
      • Survival Analysis (3cr)
      • Computational Statistics (3cr)
      • Applied Probability Theory (3cr) required for Ph.D. candidates
      • Advanced Statistical Inference (3cr) required for Ph.D. candidates
  3. At least six additional credits from approved courses, which can include any from the above list (2 courses).
  4. At least six thesis credits (STA 599). These can be taken over different semesters.

Non-Thesis Option Requirements

The non-thesis option is recommended to those students who prefer to attend a broader selection of courses, without a particular emphasis on research. Students will complete a minimum of 33 credits as follows:

  1. At least nine credits (3 courses) selected from the following required courses:
    • MTH 451 – Introduction to Probability and Statistics (3cr)
    • MTH 452 – Mathematical Statistics (3cr)
    • At least one of the following:
      • STA 501 – Analysis of Variance and Variance Components (3cr)
      • STA 502 – Applied Regression Analysis (3cr)
      • STA 576 – Econometrics (3cr)
  2. At least nine additional credits selected from (3 courses):
    • STA 500 – Analysis of Missing Data (4cr)
    • STA 501 – Analysis of Variance and Variance Components (3cr)
    • STA 502 – Applied Regression Analysis (3cr)
    • STA 515 – Spatial Data Analysis (3cr)
    • STA 525 – Programming and Data Management in SAS (4cr)
    • STA 536 – Applied Longitudinal Analysis (3cr)
    • STA 541 – Multivariate Statistical Methods (3cr)
    • STA 542 – Categorical Data Analysis Methods (3cr)
    • STA 545 – Bayesian Statistics (3cr)
    • STA 550 – Ecological Statistics (3cr)
    • STA 560 – Time Series Analysis (4cr)
    • STA 585 – Statistical Analysis of Network Data (4cr)
    • STA 592 – Special Topics in Statistics. Current courses include:
      • Survival Analysis (3cr)
      • Computational Statistics (3cr)
  3. At least six of the remaining 15 credits must be at the 500 level or above (exclusive of STA 591) (15 credits)
  4. A Written Qualifying Examination

The above coursework must include at least one course that requires a final paper involving significant independent study.

Questions should be directed to Dr. Wu (jing_wu@uri.edu).