Computer Science and Statistics

College of Arts and Sciences

Master of Science in Statistics

The Master of Science in Statistics at The University of Rhode Island is designed as a modern 2-year graduate program in statistical methods. In the last few years our program has grown, and it has attracted students from different countries, backgrounds, and with diverse research interests. Compared to other Masters programs, we have a very favorable student to faculty ratio (2 to 1) and this allows our students to receive more personalized attention and guidance from their professors. Students can choose at the time of application between a “thesis option” and a “non-thesis option”. The choice can be reversed at a later time. See below for more details on the two alternatives.

A Foreword to Prospective Applicants

Who should apply

We strongly believe that a diverse student body leads to a better and more productive learning environment and we encourage anyone with an interest in statistics to apply. While we require the GRE, we do not pre-determine a threshold, but we try to give an holistic assessment of each candidate. The quantitative section of the GRE is in fact only one of the criteria that we consider. Other aspects that will be considered include: out-of-the box thinking, GPA, performance on previous quantitative courses, involvement on research projects, industry experience, leadership ability, vision, collaborative skills, computational background, motivation to learn statistics. Strength in one or more of these areas greatly increases the chance of being admitted to our program and being considered for financial support. Our young faculty supports students through the challenges that a graduate program can present, encourages free-thinking and offers mentoring to prepare students for a great professional career. We encourage applicants to check our faculty webpage and indicate in the application statement which areas of research sound more appealing. This will highlight your motivation to apply to our program.

About our courses

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, machine learning, network data, survival analysis, latent class modeling, 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.

Other activities for students

Students are encouraged to attend the statistics colloquium. We invite renowned scholars in different fields to present their research, at a level that can be understandable and motivating. It is a unique chance for students to have a broader perspective on statistics and computational sciences.
Every year our graduate students participate, present, and often win awards at important statistical conferences like the Joint Statistical Meetings and the New England Statistical Symposium. The department aims to fund one conference for each student over the 2 years duration of the program. Chances of winning one of the travel awards are higher if students present a poster or give a talk.
During the summer of the first year, students regularly find internship opportunities and at the end of their program receive attractive job offers in the industry as data scientists, statisticians, biostatisticians, or continue their education in Ph.D. programs in statistics and related fields.

Financial support

We offer financial support for limited number of candidates on a competitive basis. Our graduate teaching assistantships covers full tuition and medical insurance, and include a generous stipend. We provide research assistantships and summer fellowships, upon availability of grant funding.

Location

The Department of Computer Science and Statistics, in the main campus of The University of Rhode Island, is conveniently located at only 35 minutes from Providence, 90 minutes from Boston and less than 3 hours from New York City. The area offers many post-master opportunities for young statisticians and data scientists, both in industry (banks, insurance companies, hi tech firms, etc.) and in academia. Brown, Harvard, Boston University, Worcester Polytechnic Institute, UConn, UMass are some of the nearby institutions that grant Ph.D. degrees in statistics, data science, or biostatistics.

Prerequisites

The following courses or their equivalents (will need approval of the graduate program chair) may be required to be admitted to our program if a student lacks a strong quantitative background. However, these requirements may be waived based on prior experience in industry or research.

  • MTH 141 – Introductory Calculus with Analytic Geometry
  • MTH 142 – Intermediate Calculus with Analytic Geometry
  • MTH 215 – Introduction to Linear Algebra
  • MTH 243 – Calculus for Functions of Several Variables
  • STA 409 – Statistical Methods in Research I
  • STA 412 – Statistical Methods in Research II

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 a the masters 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 501 – Analysis of Variance and Variance Components (3cr)
    • STA 502 – Applied Regression Analysis (3cr)
    • STA 515 – Spatial Data Analysis
    • STA 536 – Applied Longitudinal Analysis
    • 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 592 – Special Topics in Statistics. Current courses include:
      • Survival Analysis (3cr)
      • Statistical methods for network data (3cr)
      • Computational statistics (3cr)
      • Missing data analysis (3cr)
  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 501 – Analysis of Variance and Variance Components (3cr)
    • STA 502 – Applied Regression Analysis (3cr)
    • STA 515 – Spatial Data Analysis
    • STA 536 – Applied Longitudinal Analysis
    • 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 592 – Special Topics in Statistics. Current courses include:
      • Survival Analysis (3cr)
      • Statistical methods for network data (3cr)
      • Computational statistics (3cr)
      • Missing data analysis (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 Comprehensive Examination

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

Questions should be directed to Dr. Puggioni (gpuggioni@uri.edu)

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