Programs

M.S. candidates must complete 30 course credits, pass a comprehensive examination, and submit an approved essay. The required courses for the M.S. degree include 16:960:563 Regression Analysis; 16:960:582 Introduction to Methods and Theory of Probability; 16:960:583 Methods of Inference; 16:960:586 Interpretation of Data I; and 16:960:590 Design of Experiments. Requirements for the M.S. program may be satisfied in a part-time evening program.

Students may complete the M.S. program with or without one of the following three options: biostatistics, quality management, and data mining. The option in biostatistics requires 16:960:584,585 Biostatistics I,II; and either 16:960:542 Life Data Analysis or 16:960:553 Categorical Data Analysis, in addition to the general requirements of the M.S. program. The option in quality management, offered in cooperation with the graduate program in industrial and systems engineering, requires 16:960:540 Statistical Quality Control I; 16:960:542 Life Data Analysis; 16:960:591 Advanced Design of Experiments; 16:540:580 Quality Management; and 16:540:585 Systems Reliability Engineering, in addition to the general requirements of the M.S. program. The option in data mining, offered in cooperation with the graduate program in computer science, requires 16:960:567 Applied Multivariate Analysis; 16:960:587 Interpretation of Data II; 16:960:588 Data Mining; 16:198:513 Design and Analysis of Data Structures and Algorithms; and 16:198:536 Machine Learning, and waives the requirement of 16:960:590.

The Ph.D. program requires 48 course credits and a dissertation. Students are required to accumulate 24 research credits while writing their theses. Research work follows successful completion of qualifying examinations. The first qualifying examination is taken near the end of the first year of study after completion of 16:960:592 Theory of Probability and 16:960:593 Theory of Statistics. The second examination is generally taken in the second or third year of study after 16:960:652,653 Advanced Theory of Statistics I,II; 16:960:663 Regression Theory; and 16:960:680-681 Advanced Probability Theory I and II. In addition to these seven core courses for the qualifying examinations, the Ph.D. program requires 16:960:587 Interpretation of Data II; two more 3-credit courses in statistics at the 600 level; and three semesters of 16:960:693 Current Topics in Statistics. The courses 16:960:680-681 may be replaced in the curriculum and on the oral exam by two 600-level courses chosen with approval of the graduate director.

All Ph.D. candidates are required to demonstrate proficiency in one foreign language related to their chosen fields or in computer programming relevant to statistics. While there is no formal residency requirement, the faculty urges Ph.D. candidates to spend at least one full academic year in residence.

An entering Ph.D. student should have a good background in mathematics, including advanced calculus and linear algebra. These latter subjects, however, are not required to gain admission. Each student selects his or her program in conference with a department adviser. There is a wide range of course offerings and areas of research. These include statistical inference, estimation theory, operations research, hypothesis testing, decision theory, biostatistics, empirical Bayes and Bayes methods, regression analysis, analysis of variance, experimental design, multivariate analysis, nonparametric statistics, data mining, image and signal processing, statistical computing, sampling theory, robust statistics, survival analysis and incomplete data, longitudinal data, sequential analysis, quality-control theory, time-series analysis, applied probability, stochastic processes, and probability theory, including stopping rules and martingales. Information about recommended course sequences for degrees is available upon request from the office of the graduate director.

The graduate program in statistics and biostatistics offers two additional master's tracks: M.S. tracks in financial statistics and risk management (FSRM) and in data science (DS). These are listed separately in this catalog. The objective of the FSRM track is to train students to become professional statisticians/risk managers working in financial institutions and related fields. The DS track focuses on the data analytic aspects of the data science field.

The graduate program in statistics and biostatistics also collaborates with the M.S. program in mathematical finance and with the master of business and science program.

The objective of the master of business and science degree with a concentration in statistics and biostatistics is to train individuals who will assume management roles in organizations that use statistical decision-making tools and apply statistical methodology to improve management practice. These organizations are found in the pharmaceutical, manufacturing, marketing, banking, and insurance industries.

Further information on all programs may be found on the web at statistics.rutgers.edu.