MS Program Description
Prerequisites
Admission to the Statistics graduate program at Rutgers has a number of prerequisites . Applicants may be required to take some preliminary non-graduate courses. Students are not required to have taken a formal course in statistical methods; however, students whose educational background lacks a suitable course in statistical methods may be required to take 960:484. Credit for STAT 484 or any other undergraduate level courses (at Rutgers or elsewhere) cannot be counted toward the 30 credit graduation requirement.
Formal Credit Requirements
M.S. candidates must have at least 30 semester-hours of approved graduate credits of which no more than two may be C+ or lower. Students must also maintain a GPA of 3.0 or higher. If a student takes a course a second time, both the original and any repeated grades contribute to the grade point average in the standard way,
Language Requirement
There is no foreign language requirement for the MS degree.
Transfer of Credits
Up to 12 credits of such acceptable credits may be permitted to be applied for the MS degree. This is subject to individual consideration.
A Guide for Courses and Electives for M.S. in Applied and Mathematical Statistics.
The Syllabi for the most recent versions of these courses can be found here.
Required Courses:
563 Regression Analysis (Fall)
582 Introduction to Methods and Theory of Probability (Fall)
583 Methods of Statistical Inference (Spring)
Electives:
540 Statistical Quality Control (Summer)
542 Life Data Analysis (Spring)
545 Statistical Practice (Fall)
553 Categorical Data Analysis (Fall)
555 Nonparametric Statistics (Fall)
565 Applied Time Series Analysis (Spring)
567 Applied Multivariate Analysis (Spring)
568 Bayesian Data Analysis (Spring)
576 Survey Sampling (Spring)
588 Data Mining (Fall)
590 Design of Experiments (Fall)
Approved graduate courses offered by other departments related to statistics and machine learning include, but are not necessarily limited to:
16:198:512 Design/Analysis of Data Structure and Algorithm (Fall & Spring, Computer Science Department)
16:198:520 Intro to Artificial Intelligence (Fall & Spring, Computer Science Department)
16:198:536 Machine Learning (Spring, Computer Science Department)
16:332:443 Machine Learning For Engineers (ECE Department)
16:332:530 Introduction to Deep Learning (ECE Department)
26:711:685 Special Topics In Management Science - Optimization Methods For Machine Learning (Fall 2024)
(i) 582 and 583 can be replaced by 592 and 593, their Ph.D. level counterparts respectively, subject to approval of the graduate directors and the course instructors.
(ii) Students can only have credit for one of 582, 592.
(iii) Students can only have credit for one of 583, 593.
Research Requirements
The M.S. degree requires the submission of a paper on some topic in statistics. Ordinarily, this will be satisfied by the submission of an acceptable paper done as a course project.
Examination Requirements
Upon completion of courses, the M.S. candidate must pass a written examination covering the basic material of taken course work. This exam is in two parts, with theory and applications tested separately. A student may attempt this exam no more than 4 times.
Past MS Exams & Solutions
Past exams and solutions can be found here:
https://rutgersconnect.sharepoint.com/sites/stat-ms_phd_past_exams Go to documents.
To register send an email to Eileen Sharkey,
