**Chairperson: Anne Clough, Ph.D.
Program Director: Cheng-Han Yu, Ph.D.**

Applied Statistics website

## Degree Offered

Master of Science, students are admitted under Plan B (non-thesis option) but may request Plan A (thesis option).

## Program Description

The master of science program in applied statistics offered by the Department of Mathematical and Statistical Sciences teaches students the science of data. It produces graduates who deal with big data, perform statistical analysis to detect hidden patterns in data, perform risk factor analysis and perform predictive analysis. Statistical data science utilizes principled foundations in mathematics with applications in many fields such as social sciences, engineering, business, biomedical sciences and economics. The master of science in applied statistics is intended for students who have a mathematical background (not necessarily having a degree in mathematics) that want to develop strong data analytic skills to solve complex, real world problems. This program meets the needs for recent graduates who are seeking a master’s degree program in applied statistical science, and for mid-career workers with a solid quantitative background who are seeking a graduate program in applied statistics either for career advancement or for a career change. Students may select courses from a large number of approved courses offered by the Department of Mathematical and Statistical Sciences and other units on campus.

Students may pursue the degree on a full-time or part-time basis. Full-time, dedicated students can complete the degree in as little as 12 months. Most courses are offered in the evenings.

## Prerequisites for Admission

Applicants must have completed or are in the process of completing a bachelor's degree from an accredited college or university. Admission to the master of science program in applied statistics is based on a sufficient formal mathematics and/or statistics background and a previous experience with programming. The applied statistics program accommodates students from a very wide variety of disciplines. Students can be admitted with background courses to be completed early in the program.

## Application Deadline

The master of science program in applied statistics follows the Graduate School deadlines for the submission of applications: August 1 for fall admission, December 15 for spring admission, and May 1 for summer admission. However, to be considered for financial aid, applications for fall must be submitted by January 15. Decisions about acceptance into the program are made when all required documents for the application are received. Admission decisions are made independently of decisions to offer financial aid.

## Application Requirements

Applicants must submit, directly to the Graduate School:

- A completed online application form and fee.
- Copies of all college/university transcripts except Marquette.
^{1} - A statement of purpose outlining rationale for seeking admission to this program, possible areas of interest, relevant work experience or education, and career goals.
- Three letters of reference from professors or professionals familiar with the applicant's abilities, academic work and/or professional background.
- (For international applicants who have not attended an English-speaking university only) a TOEFL score or other acceptable proof of English proficiency.

Optional application requirement: GRE scores (General Test only).

^{1} | Upon admission, final official transcripts from all previously attended colleges/universities, with certified English translations if original language is not English, must be submitted to the Graduate School within the first five weeks of the term of admission or a hold preventing registration for future terms is placed on the student record. |

## General Information

Students interested in applying to the program should consult the program website for additional information, including a list of currently approved courses for the degree.

A complete list and short description of the courses offered by the Department of Mathematical and Statistical Sciences (MSSC) is available on the MSSC department page of the bulletin.

## Applied Statistics Master of Science

A master’s student is admitted to the non-thesis program (Plan B). A formal request to pursue a thesis (Plan A) must be approved by the department's applied statistics program director, the department’s Graduate Committee and the Graduate School.

## Plan A

All Plan A students must complete a minimum of 30 credit hours (21 credits of program core courses, 3 approved elective credits and 6 credits of MSSC 6999 Master's Thesis), and submit a thesis that must be an original contribution to the student's field of study. A public defense of the thesis is required.

Code | Title | Hours |
---|---|---|

Required Core courses (21 credits): | ||

MSSC 5710 | Mathematical Statistics | 3 |

MSSC 5780 | Regression Analysis | 3 |

MSSC 6010 | Computational Probability | 3 |

MSSC 6020 | Statistical Simulation | 3 |

MSSC 6040 | Applied Linear Algebra | 3 |

MSSC 6240 | Design and Analysis of Scientific Experiments | 3 |

MSSC 6250 | Statistical Machine Learning | 3 |

Approved Elective course(s) | 3 | |

Master's Thesis/Research: | ||

MSSC 6999 | Master's Thesis | 6 |

Total Credit Hours: | 30 |

## Plan B

All Plan B students complete a minimum of 30 credits (21 credits of program core courses, 6 approved elective credits and a 3-credit statistical consulting practicum). A written report and an oral presentation are required for the statistical consulting practicum.

Code | Title | Hours |
---|---|---|

Required Core courses (21 credits): | ||

MSSC 5710 | Mathematical Statistics | 3 |

MSSC 5780 | Regression Analysis | 3 |

MSSC 6010 | Computational Probability | 3 |

MSSC 6020 | Statistical Simulation | 3 |

MSSC 6040 | Applied Linear Algebra | 3 |

MSSC 6240 | Design and Analysis of Scientific Experiments | 3 |

MSSC 6250 | Statistical Machine Learning | 3 |

Approved Elective courses | 6 | |

Professional Practice/Statistical Consulting: | 3 | |

Practicum in Applied Statistics and Data Science | ||

Total Credit Hours: | 30 |

Plan A and Plan B master's students may select additional approved elective courses from within the department or from outside departments. For a complete list of approved elective courses outside of the department, consult with the applied statistics program director. The following is a list of approved elective courses within the department:

Code | Title | Hours |
---|---|---|

MSSC 5540 | Numerical Analysis | 3 |

MSSC 5630 | Mathematical Modeling and Analysis | 3 |

MSSC 5700 | Theory of Probability | 3 |

MSSC 5750 | Computational Statistics | 3 |

MSSC 5760 | Time Series Analysis | 3 |

MSSC 5790 | Bayesian Statistics | 3 |

MSSC 6000 | Scientific Computing | 3 |

MSSC 6030 | Applied Mathematical Analysis | 3 |

MSSC 6210 | Theory of Statistics | 3 |

MSSC 6230 | Multivariate Statistical Analysis | 3 |

**For both Plan A and Plan B:**

- Depending on the course topic and approval by program director, MSSC 5931 Topics in Mathematical or Statistical Sciences, MSSC 6931 Topics in Mathematical or Statistical Sciences, MSSC 6995 Independent Study in Mathematical or Statistical Sciences or MSSC 6960 Seminar in Mathematical or Statistical Sciences may also be an approved elective course.
- A maximum of 6 credit hours of courses may be taken from the Medical College of Wisconsin and the University of Wisconsin-Milwaukee, as allowed under the reciprocal arrangement between Marquette and these institutions, as long as they are pre-approved by the applied statistics program director and the Graduate School.
- Master's students who come with little background in statistics may be required to complete MSSC 5720 Statistical Methods or other supportive courses during their first year or during the summer before their first fall term. This is in addition to the required course work for Plan A or Plan B.

## Accelerated Bachelor's-Master's Degree Program

The accelerated degree program (ADP) in applied statistics allows students to earn, in five years, a bachelor's degree with an undergraduate major in a variety of fields including, but not limited to: bioinformatics, biomedical engineering, business, chemistry, computational mathematics, computer science, data science, electrical engineering, mathematics, applied mathematical economics, mechanical engineering, physics and psychology, along with the master of science degree in applied statistics. Students complete 12 credit hours of approved graduate courses while an undergraduate student that count as part of the undergraduate credit hour requirement.

Students may obtain both degrees in five years. Students with a GPA of 3.000 or better in their mathematics, science and engineering courses are eligible to apply when they reach junior standing. Up to 12 graduate credits can count toward both degrees. In order to count credits toward both degrees, students must be admitted to the ADP before enrolling in the courses they wish to have applied to both degrees.

The Department of Mathematical and Statistical Sciences offers early admission into the master of science in applied statistics program. Marquette undergraduate students majoring in the above listed majors and others can apply for this program when they reach junior standing.

### Courses

**MSSC 5020. The Teaching of Mathematics. 3 cr. hrs.**

Historical background, problems, curricular materials, and teaching procedures in the various areas of mathematics pertinent to the needs of a secondary school mathematics teacher. In addition, a three-hour time block on one day each week between 8 a.m. and 3 p.m. must be kept free for clinical experience.

**MSSC 5030. Concepts in Geometry and Calculus from an Advanced Standpoint. 3 cr. hrs.**

Topics chosen primarily from geometry and calculus, taught from an advanced standpoint to enrich and deepen the student's understanding. Emphasis on alternative approaches, generalizations, historical contexts and connections with prior mathematical studies.

**MSSC 5040. Concepts in High School Algebra and Number Theory from an Advanced Standpoint. 3 cr. hrs.**

Topics closely related to the high school mathematics curriculum, chosen primarily from algebra and number theory, taught from an advanced standpoint to enrich and deepen the student's understanding. Emphasis on alternative approaches, generalizations, historical contexts and connections with prior mathematical studies.

**MSSC 5120. Abstract Algebra 1. 3 cr. hrs.**

Sets, mappings, operations on sets, relations and partitions. A postulational approach to algebraic systems including semigroups, groups, rings and fields. Homomorphisms of groups and rings, number systems, polynomial rings.

**MSSC 5121. Abstract Algebra 2. 3 cr. hrs.**

A continuation of MSSC 5120 with emphasis on groups, rings, fields and modules.

**MSSC 5200. Intermediate Analysis 1. 3 cr. hrs.**

Limits and continuity, differentiability, Riemann integration. Topology of N-dimensional spaces.

**MSSC 5201. Intermediate Analysis 2. 3 cr. hrs.**

Transformations of N-spaces, line and surface integrals, sequences and series, uniform convergence.

**MSSC 5210. Complex Variables. 3 cr. hrs.**

Complex numbers, analytic functions, differentiation, series expansion, line integrals, singularities and residues.

**MSSC 5310. History of Mathematical Ideas. 3 cr. hrs.**

Topics selected from the following: development of the number system (need for irrational and complex numbers); development of geometry including the effects of the discovery of non-Euclidean geometry; limit concept; need for axiomatic structures; twentieth-century problems. Current mathematics research and place of mathematics in today's world.

**MSSC 5320. Theory of Numbers. 3 cr. hrs.**

Integers, unique factorization theorems, arithmetic functions, theory of congruences, quadratic residues, partition theory.

**MSSC 5420. Foundations of Geometry. 3 cr. hrs.**

Modern postulational development of Euclidean and non-Euclidean geometries.

**MSSC 5450. Topology. 3 cr. hrs.**

Topological spaces, mappings, metric spaces, product and quotient spaces. Separation axioms, compactness, local compactness and connectedness.

**MSSC 5500. Theory of Differential Equations. 3 cr. hrs.**

Existence and uniqueness theorems, linear and non-linear systems, numerical techniques, stability.

**MSSC 5510. Elementary Partial Differential Equations. 3 cr. hrs.**

Fourier series, method of separation of variables, eigenfunction expansions, application of eigenfunctions to partial differential equations, Green's functions and transform methods.

**MSSC 5540. Numerical Analysis. 3 cr. hrs.**

Numerical solution of algebraic and transcendental equations, linear systems and the algebraic eigenvalue problem, interpolation and approximation, numerical integration, difference equations, numerical solution of differential equations and finite difference methods.

**MSSC 5630. Mathematical Modeling and Analysis. 3 cr. hrs.**

Construction and analysis of mathematical models from biological, behavioral and physical sciences.

**MSSC 5650. Theory of Optimization. 3 cr. hrs.**

Fundamental theorems describing the solution of linear programs and matrix games. Minimax, duality, saddle point property, simplex and specialized algorithms. Zero sum games, transportation and assignment problems, applications to economics.

**MSSC 5670. Applied Combinatorial Mathematics. 3 cr. hrs.**

Permutations and combinations, recurrence relations, inclusions and exclusion, Polya's theory of counting, graph theory, transport networks, matching theory.

**MSSC 5700. Theory of Probability. 3 cr. hrs.**

Random variables, distributions, moment generating functions of random variables, various derived probabilistic models and applications.

**MSSC 5710. Mathematical Statistics. 3 cr. hrs.**

Sampling theory and distributions, estimation and hypothesis testing, regression, correlation, analysis of variance, non-parametric methods, Bayesian statistics.

**MSSC 5720. Statistical Methods. 3 cr. hrs.**

Probability, discrete and continuous distributions. Treatment of data, point and interval estimation, hypothesis testing. Large and small sample method, regression, non-parametric methods. An introduction to the basic understanding of statistical methods. Applications-oriented.

**MSSC 5730. Introduction to R for Statistics and Data Science. 1 cr. hr.**

An introductory course to the statistical analysis software R. Topics include basic R programming, importing and cleaning data, data visualization, performing descriptive and inferential statistics, and creating reproducible reports.

**MSSC 5740. Biostatistical Methods and Models. 3 cr. hrs.**

Introduction to the statistics of life science and the use of mathematical models in biology. Data analysis and presentation, regression, analysis of variance, correlation, parameter estimation and curve fitting. Biological sequence analysis, discrete and continuous mathematical models and simulation.

**MSSC 5750. Computational Statistics. 3 cr. hrs.**

Explores computational data analysis, an essential part of modern statistics. Introduces statistical computing including statistical programming, Monte Carlo simulation and parallel computing, smoothing and density estimation, implementing numerical methods in R (e.g., Expectation-Maximization algorithm), fitting models to data, statistical prediction and cross-validation.

**MSSC 5760. Time Series Analysis. 3 cr. hrs.**

Basic concepts of probability. Stationary time series. Autocorrelation and spectrum. Descriptive methods for time series data. ARMA and ARIMA models: estimation and forecasting. Identification and diagnostic techniques. Periodogram and spectral analysis. Use of software for time series analysis.

**MSSC 5780. Regression Analysis. 3 cr. hrs.**

Basic concepts of statistical inference, simple linear regression, multiple linear regression, diagnostic analysis, selecting the best equation, stepwise methods, nonlinear regression, use of statistical software.

**MSSC 5790. Bayesian Statistics. 3 cr. hrs.**

Bivariate, conditional and marginal distributions. The Bayesian philosophy, quantification of a priori information, prior, likelihood and posterior distributions. Bayesian linear models, posterior parameter estimation including maximum posteriori and marginal expectations. Topics may include numerical integration and Markov chain Monte Carlo techniques. Use of a high-level software package.

**MSSC 5931. Topics in Mathematical or Statistical Sciences. 1-3 cr. hrs.**

Topics selected from one of the various branches of mathematics or statistics. Specific topics to be announced in the Schedule of Classes.

**MSSC 6000. Scientific Computing. 3 cr. hrs.**

Foundational methods and techniques of scientific computing in the mathematical and statistical sciences. Covers fundamental computational algorithms aimed toward applications in science and engineering. Students implement algorithms, and visualize and validate their outcomes. Further, students are introduced to and implement best programming practices. Prereq: Calculus course or cons. of instr.; introductory statistics course or cons. of instr.; and programming competency in a high-level language.

**MSSC 6010. Computational Probability. 3 cr. hrs.**

A modern course in probability. Foundations of probability for modeling random processes with computational techniques. Topics include counting techniques, probability of events, random variables, distribution functions, probability functions, probability density functions, expectation, moments, moment generating functions, special discrete and continuous distributions, sampling distributions, transformation of variables, prior and posterior distributions, Law of Large Numbers, Central Limit Theorem, the Bayesian paradigm. Numerical and computational methods will be covered throughout topics. Prereq: Three semesters of mathematics beyond calculus and MATH 4720 or equiv.

**MSSC 6020. Statistical Simulation. 3 cr. hrs.**

Elements of statistical simulation and modeling with applications. Generation of random variables, simulating statistical models, Monte Carlo method, Markov chains, birth-and-death processes, queues, variance reduction, Markov chain Monte Carlo (MCMC) methods and applications, bootstrapping, validation and analysis of simulated data. Prereq: MSSC 6010 and programming competency in a high-level language.

**MSSC 6030. Applied Mathematical Analysis. 3 cr. hrs.**

Foundational topics in analysis considered from a modeling and numerical viewpoint. Emphasizes techniques of proof and approximation, and their role in the solution of problems arising in applications. Prereq: Multivariable calculus and linear algebra.

**MSSC 6040. Applied Linear Algebra. 3 cr. hrs.**

Foundational linear algebra considered from a numerical viewpoint. Focuses on solutions of linear systems of equations, eigenvalues and eigenvectors, and transformations. Emphasizes and illustrates proof and numerical implementation using problems arising in applications. Prereq: Multivariable calculus and linear algebra.

**MSSC 6090. Research Methods/Professional Development. 1 cr. hr.**

Designed to introduce the process of research and communication of research in the mathematical and statistical sciences, including presentation and publication of research, preparation of grant proposals, and ethical considerations. May be repeated.

**MSSC 6110. Applied Discrete Mathematics. 3 cr. hrs.**

Applied discrete mathematics for the mathematics, engineering and computer science graduate student. Emphasis on graph theory and counting problems that serve as a foundation for research areas in the second term. Theory and applications are covered for topics including trees, graph coloring, chromatic polynomials, generating functions, recurrence relations, distinct colorings and Polya's Theorem. Prereq: COSC 1020 and MATH 1450 or equiv.; MATH 1451 and MATH 2100 or equiv.

**MSSC 6120. Optimization. 3 cr. hrs.**

Principles of deterministic model building in operations research. Linear programming and duality. Dynamic and integer programming. Nonlinear optimization and parameter estimation. Prereq: MATH 3100 or equiv.

**MSSC 6130. Dynamical Systems. 3 cr. hrs.**

Theory of discrete and continuous dynamical systems. Periodic solutions, bifurcations, chaotic systems, attractors, fractal dimension and simulation of these systems. Prereq: MATH 4200 or equiv.

**MSSC 6210. Theory of Statistics. 3 cr. hrs.**

Brief review of sampling distributions, convergence, Central Limit Theorem and Law of Large Numbers. Estimation, testing hypotheses, regression and correlation analysis, non-parametric methods. Prereq: MATH 4700 or equiv.

**MSSC 6220. Analysis of Variance and Covariance. 3 cr. hrs.**

Review of statistical inference. One-way layout and multiple comparison. Two-, three- and higher-way layouts. Latin squares, incomplete block and nested design. Analysis of covariance. Prereq: MATH 4710 or equiv.

**MSSC 6230. Multivariate Statistical Analysis. 3 cr. hrs.**

Basic properties of random vectors, multivariate normal distribution, estimations of mean vector and covariance matrix, Wishart distribution, hypothesis testing, Hotelling's T2, multivariate analysis of variance, principal component analysis, factor analysis, canonical correlation analysis, classification and discriminant analysis. A high level programming language may be used. Prereq: MATH 3100 or equiv; MATH 4710 or equiv.

**MSSC 6240. Design and Analysis of Scientific Experiments. 3 cr. hrs.**

Single factor, two-factor and multi-factor designs and their analysis, Latin-square design and its analysis; power analysis and sample size selection; 2^k factorial designs; confounding/blocking designs; orthogonality and orthogonal contrasts; 3^k factorial designs; response surface methodology. Prereq: A course in statistical methods, such as MATH 4720 or equiv.

**MSSC 6250. Statistical Machine Learning. 3 cr. hrs.**

Multivariate data and exploratory analysis, random vector and multivariate normal distribution, multivariate linear regression, principal component and other dimensional reduction techniques, linear discriminant analysis, recursive partition and tree-based methods including classification tree and regression tree, cluster analysis, neural network and support vector machine. Prereq: A course in statistical methods, such as MATH 4720, and a course in linear algebra, such as MATH 3100, MATH 4780 or equiv., cons. of instr.

**MSSC 6410. Real Analysis. 3 cr. hrs.**

Involves study of algebraic structures of real analysis, function spaces, introduction to linear operators, measure and integration theory, convergence theorems, limits, continuity and derivatives. Prereq: MATH 4200.

**MSSC 6420. Algebra. 3 cr. hrs.**

Studies groups, rings, fields and vector spaces including Sylow's theorems, field of quotients of an integral domain, structure of finitely generated modules over a principal ideal domain, Galois theory of equations, ordered fields and classical groups. Prereq: MATH 4120 or equiv.

**MSSC 6430. Logic and Set Theory. 3 cr. hrs.**

Naive set theory, first-order logic, elementary model theory, non-standard analysis, Godel's incompleteness theorems for elementary arithmetic, axioms for set theory, ordinal and cardinal arithmetic, the continuum hypothesis, methods of inner models and forcing for proving consistency and independence results. Prereq: MATH 4120 or equiv.

**MSSC 6440. Topology. 3 cr. hrs.**

Metric spaces, fundamental topology notions, subspace topology, product spaces, quotient spaces, separation axioms, Tietze's theorem, compactness, metrization, uniform spaces, function spaces, homotopy relation, fundamental group, computing manifold groups. Prereq: MATH 4200 or equiv.

**MSSC 6770. Innovations in Secondary Mathematics: Meeting the NCTM Standards. 3 cr. hrs.**

Online course designed for teachers of secondary mathematics. Emphasizes relevant NCTM standards through discussion, projects, and implementation in a secondary mathematics classroom. Mathematics content amplifies and extends selected topics of secondary mathematics. Topics vary. Credit may be earned multiple times if completed under a different topic. Prereq: Cons. of dept. ch.; one term of calculus and access to an algebra or geometry class of secondary students; or cons. of course coordinator; admitted to MSST or College of Education.

**MSSC 6931. Topics in Mathematical or Statistical Sciences. 3 cr. hrs.**

Topics vary. Multiple enrollments allowed under different topics.

**MSSC 6952. Colloquium in Mathematical or Statistical Sciences. 1 cr. hr.**

Research and scholarly presentations on selected topics in the mathematical or statistical sciences by visiting researchers, departmental faculty and graduate students. Prereq: Grad. stndg.

**MSSC 6953. Seminar in Mathematics Curriculum Development and Material 1. 3 cr. hrs.**

The historical evolution of mathematics learning theories and research-generated conceptions of mathematics learning; comparisons of various learning theories and their impact on research in mathematics learning; implications of research and learning theories on curriculum development; implications of mathematics learning research/theories on the teaching and learning of mathematics. Prereq: Admitted to MSST or College of Education.

**MSSC 6954. Seminar in Mathematics Curriculum Development and Material 2. 3 cr. hrs.**

Philosophy of education with particular attention to mathematics education; development by students of useful curricula in the form of teaching units, evaluation materials, and student and teacher bibliographies for specific topics, grade levels, and ability groups; aspects of supervision as related to the role of department chairperson. Prereq: MSSC 6953; admitted to MSST or College of Education.

**MSSC 6960. Seminar in Mathematical or Statistical Sciences. 3 cr. hrs.**

Topics selected from one of the various branches of mathematics or statistics. Specific topics are announced in the Schedule of Classes.

**MSSC 6974. Practicum for Research in Mathematical or Statistical Sciences. 1-3 cr. hrs.**

S/U grade assessment. Prereq: Cons. of dept. ch.

**MSSC 6975. Practicum in Applied Statistics and Data Science. 3 cr. hrs.**

Provides students with the opportunity to explore real-world examples of data analysis as a statistical consultant. Prereq: 3.000 MU GPA; completed at least 12 credit hours; cons. of the applied statistics dir. of graduate studies; or cons. of dept. ch.

**MSSC 6995. Independent Study in Mathematical or Statistical Sciences. 1-5 cr. hrs.**

Faculty-supervised, independent study/research of a specific area or topic in mathematics or statistics. Prereq: Cons. of instr. and cons. of dept. ch.

**MSSC 6998. Professional Project in Mathematical or Statistical Sciences. 0 cr. hrs.**

SNC/UNC grade assessment. Prereq: Cons. of dept. ch.

**MSSC 6999. Master's Thesis. 1-6 cr. hrs.**

S/U grade assessment. Prereq: Cons. of dept. ch.

**MSSC 8995. Independent Study in Mathematical or Statistical Sciences. 1-3 cr. hrs.**

In-depth research on a topic or subject matter usually not offered in the established curriculum with faculty and independent of the classroom setting. Prereq: Cons. of instr. and cons. of dept. ch.

**MSSC 8999. Doctoral Dissertation. 1-12 cr. hrs.**

S/U grade assessment. Prereq: Cons. of dept. ch.

**MSSC 9970. Graduate Standing Continuation: Less than Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Designated as less than half-time status only, cannot be used in conjunction with other courses, and does not qualify students for financial aid or loan deferment. Prereq: Cons. of dept. ch.

**MSSC 9974. Graduate Fellowship: Full-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed. Prereq: Cons. of dept. ch.

**MSSC 9975. Graduate Assistant Teaching: Full-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed. Prereq: Cons. of dept. ch.

**MSSC 9976. Graduate Assistant Research: Full-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed. Prereq: Cons. of dept. ch.

**MSSC 9987. Doctoral Qualifying Examination Preparation: Less than Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week toward their doctoral qualifying exam. Prereq: Cons. of dept. ch.

**MSSC 9988. Doctoral Qualifying Examination Preparation: Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week toward their doctoral qualifying exam. May be taken in conjunction with credit-bearing or other non-credit courses to result in the status indicated, as deemed appropriate by the department. Prereq: Cons. of dept. ch.

**MSSC 9989. Doctoral Qualifying Examination Preparation: Full-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week toward their doctoral qualifying exam. May be taken in conjunction with credit-bearing or other non-credit courses to result in the status indicated, as deemed appropriate by the department. Prereq: Cons. of dept. ch.

**MSSC 9991. Professional Project Continuation: Less than Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week on their professional project. Any professional project credits required for the degree should be completed before registering for non-credit Professional Project Continuation. Prereq: Cons. of dept. ch.

**MSSC 9992. Professional Project Continuation: Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week on their professional project. Any project credits required for the degree should be completed before registering for non-credit Professional Project Continuation. Prereq: Cons. of dept. ch.

**MSSC 9993. Professional Project Continuation: Full-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week on their professional project. Any professional project credits required for the degree should be completed before registering for non-credit Professional Project Continuation. Prereq: Cons. of dept. ch.

**MSSC 9994. Master's Thesis Continuation: Less than Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation. Prereq: Cons. of dept. ch.

**MSSC 9995. Master's Thesis Continuation: Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation. Prereq: Cons. of dept. ch.

**MSSC 9996. Master's Thesis Continuation: Full-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation. Prereq: Cons. of dept. ch.

**MSSC 9997. Doctoral Dissertation Continuation: Less than Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation. Prereq: Cons. of dept. ch.

**MSSC 9998. Doctoral Dissertation Continuation: Half-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation. Prereq: Cons. of dept. ch.

**MSSC 9999. Doctoral Dissertation Continuation: Full-Time. 0 cr. hrs.**

Fee. SNC/UNC grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation. Prereq: Cons. of dept. ch.