The information contained on this website is for informational purposes only. The Undergraduate Record and Graduate Record represent the official repository for academic program requirements. These publications may be found at http://records.ureg.virginia.edu/index.php
 

Program of Study

The M.S. program requires 30 hours of graded coursework covering the breadth of applied and theoretical statistics, together with statistical consulting.  M.S. students also must pass examinations that assess computing, data analysis, and presentation skills. The M.S. program includes options to complete the M.S. with a Data Analytic Methods concentration or with a Biostatistics concentration.  The M.S. program is normally completed within three semesters.

Preparation

Students should have 3 semesters of calculus (including multiple integrals), linear algebra (comparable to UVA courses Math 3350, MATH 3351, or APMA 3080), and calculus-based probability and statistics (comparable to UVA courses MATH 3100/STAT 3120 or APMA 3100/3120).

Overview of Requirements

The successful completion of the following courses and examinations are required for completion of the M.S. in Statistics. 

Course requirements:

• Each of STAT 6120 (Linear Models), STAT 6190 (Introduction to Mathematical Statistics), and STAT 7100 (Introduction to Advanced Statistical Inference), and a course in professional skills, STAT 7995 (Statistical Consulting) (12 credits)
• At least four courses from a list of Core Electives (12 or more credits)
• At least two courses from a list of Free Electives (6 or more credits)

Examination requirements:

• Computer Skills Confirmation Test
• Master’s Final Exam
   

Core Electives

• STAT 6160 (Experimental Design)
• STAT 5330 (Data Mining)
• STAT 6130 (Applied Multivariate Statistics)
• STAT 5140 (Survival Analysis and Reliability)
• STAT 5170 (Time Series Analysis)
• STAT 5350 (Applied Causal Inference)
• STAT 6020 (Optimization and Monte Carlo Methods in Statistics and Machine Learning)
• STAT 6260 (Categorical Data Analysis)
• STAT 6250 (Longitudinal Data Analysis)
• STAT 5390 (Exploratory Data Analysis)
• STAT 5430 (Statistical Computing with Python and R)
• STAT 6440 (Bayesian Methods)
• STAT 5630 (Statistical Machine Learning)
• STAT 5180 (Survey Sampling Methods)
• STAT 7130 (Generalized Linear Models)

Free Electives

• STAT 5310 (Clinical Trials)
• STAT 5265 (Investment Science I)
• STAT 5266 (Investment Science II)
• STAT 5340 (Bootstrap and Other Resampling Methods)
• STAT 5559 (Modeling in Biology and Medicine)
• STAT 8320 (Topics in Biostatistics)
• Any from the list of core electives

Other graduate-level, three-credit courses from statistics or another department may substitute as a free elective course, subject to approval by the Director of Graduate Studies. Duplications are not allowed between 5000- and 6000-level versions of the same topic.

Data Analytic Methods Concentration

In addition to the M.S. requirements described above, the Data Analytic Methods concentration requires that the four Core Elective courses be fulfilled with STAT 5330 (Data Mining), STAT 6130 (Applied Multivariate Statistics), STAT 5630 (Statistical Machine Learning), and STAT 5430 (Statistical Computing with Python and R).  Those in this concentration may substitute SYS 6018 (Data Mining) for STAT 5330 and SYS 6016 (Machine Learning) for STAT 5630. These four courses are typically offered once per year.

Biostatistics Concentration

In addition to the M.S. requirements described above, the Biostatistics concentration requires that at least two Core Elective courses be fulfilled from among STAT 5140 (Survival Analysis), STAT 6160 (Experimental Design), STAT 6250 (Longitudinal Data Analysis), and STAT 6260 (Categorical Data Analysis).  These four courses are typically offered on a rotating two-year schedule with one offered each term.

Examination Schedule

Computer Skills Confirmation Test 

The purpose of the Computer Skills Confirmation Test is to assess whether the student is prepared for hands-on, data-intensive study of statistical methodology and applications, especially regarding the use of statistical software. The content is elementary data set management and elementary statistical analysis applications using R and Python. Students are presented with a problem whose solution requires substantial manipulation of a large or complicated data set. This is a timed written exam whose format is announced near the beginning of the Fall semester. The exam is offered once per year, near the end of the Fall semester.

Students are required to take the Computer Skills Confirmation Test in the first semester of the program unless prior approval to delay the exam is given by the Director of Graduate Studies. A student who fails the Computer Skills Confirmation Test may sit for the test again at the discretion of the faculty.

Master’s Final Exam 

The purpose of this exam is to assess the student’s professional maturity and aptitude in using statistical techniques to produce sensible answers to data-analysis problems. The student is examined in three areas: (i.) skills in data manipulation and exploration (ii.) use and understanding of statistical methodology (iii.) skills in the presentation of data-analysis results. The Master’s Final Exam is administered as a “Statistics Poster Fair” in which all M.S. students gather in a single public event to present results of a self-developed project. Students present their results individually in the format of a poster and remain available for the duration of the fair for detailed questioning from statistics faculty and other participants. The exam is scheduled once per year, during the first half of April.

A student who fails the Master’s Final Exam or Computer Skills Confirmation Test may sit for the examination again at the discretion of the faculty.

Sample Programs

Below are sample programs for the M.S. in Statistics. Note that the indicated courses and semesters are not the only way to proceed through the program.
 

M.S. in Statistics, no concentration

M.S. in Statistics, Data Analytic Methods concentration

M.S. in Statistics, Biostatistics concentration