Applied Statistics and Data Analytics Bachelor of Science Degree
Applied Statistics and Data Analytics
Bachelor of Science Degree
 RIT /
 Rochester Institute of Technology /
 Academics /
 Applied Statistics and Data Analytics BS
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School of Mathematics and Statistics
RIT’s bachelor of science in applied statistics provides you with a strong foundation in statistical methodology, experience in its applications, a solid background in the use of statistical computing packages, and the skills to collaborate on projects that rely on statistical analysis.
^{$}58K
Median FirstYear Salary of RIT Graduates from this degree
47^{%}
Job postings that require skills in statistical software like SAS or R
34^{%}
Employment growth expected for statisticians by 2026, four times faster than the overall labor market
^{#}1
Ranking for Statisticians in Best Business Jobs List, U.S. News & World Report, 2020
Overview for Applied Statistics and Data Analytics BS
Why Study Applied Statistics and Data Analytics at RIT
 A Robust Community: Join PiRIT, a student club that fosters a community of students and faculty in mathematics and statistics.
 Career Connections: Network with recruiters from National Labs and federallyfunded Research Centers to explore coop, internship, research, and fulltime employment opportunities.
 Gain Work Experience: Complete a coop or internship, engage in undergraduate research, or study abroad to gain realworld experience.
 Jobs at Industry Leading Companies: Recent graduates are employed at Freehold Capital Management, Qool Media, Excellus BlueCross BlueShield, 3M, and General Electric.
What is Applied Statistics?
Applied statistics is data analysis. It’s managing, analyzing, interpreting, and drawing conclusions from data in order to make sound decisions in a wide range of fields, including engineering, business, health care, government, retail and commercial enterprises, and more. In applied statistics, you’ll use data to identify problems and through the analysis of this data, determine solutions and opportunities.
RIT’s Bachelor of Science in Applied Statistics and Data Analytics
RIT’s bachelor of science in applied statistics gives you an advantage in the fields of business, government, and industry, and prepares you for advanced graduate studies. Diverse application areas for graduates include product design, biostatistics, data analytics, quality control, and statistical forecasting.
Courses in Applied Statistics
Early courses in the statistics bachelors are designed to give you a foundation in calculus, statistics, algebra, and computer science. You will graduate with:
 A strong foundation in statistical methodology and experience in its applications
 A solid background in the use of statistical computing packages
 The skills to collaborate on projects that rely on statistical analysis.
Furthering Your Education in Applied Statistics
Graduate programs offered by the School of Mathematics and Statistics introduce students to rigorous advanced applied mathematical and statistical methodology. Students realize the potential for that cuttingedge methodology as a general tool in the study of exciting problems in science, business, and industry.
Combined Accelerated Bachelor’s/Master’s Degrees
Today’s careers require advanced degrees grounded in realworld experience. RIT’s Combined Accelerated Bachelor’s/Master’s Degrees enable you to earn both a bachelor’s and a master’s degree in as little as five years of study, all while gaining the valuable handson experience that comes from coops, internships, research, study abroad, and more.
 Applied Statistics and Data Analytics BS/ Applied and Computational Mathematics MS
 Applied Statistics and Data Analytics BS/ Applied Statistics MS
 +1 MBA: Students who enroll in a qualifying undergraduate degree have the opportunity to add an MBA to their bachelor’s degree after their first year of study, depending on their program. Learn how the +1 MBA can accelerate your learning and position you for success.
Advanced Degrees in Mathematics and Analytics
Students in the applied mathematics bachelor’s degree are exposed to rigorous advanced applied mathematical and statistical methodology as a tool in the study of exciting problems in science, business, and industry. Many undergraduate students choose to continue their education with one of RIT's advanced degrees in mathematics or analytics:
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Careers and Cooperative Education
Typical Job Titles
Statistician  Biostatistician 
Data Scientist  Quantitative Analyst 
Data Engineer  Business Analytics Associate 
Quality Analyst  Research Analyst 
Reporting and Data Analytics Specialist 
Industries

Biotech and Life Sciences

Defense

Government (Local, State, Federal)

Health Care

Insurance

Investment Banking

Telecommunications
Cooperative Education
What’s different about an RIT education? It’s the career experience you gain by completing cooperative education and internships with top companies in every single industry. You’ll earn more than a degree. You’ll gain realworld career experience that sets you apart. It’s exposure–early and often–to a variety of professional work environments, career paths, and industries.
Coops and internships take your knowledge and turn it into knowhow. Experiential learning opportunities in statistics include a range of handson experiences, from coops and internships to undergraduate research that enable you to apply your statistical knowledge in professional settings while you make valuable connections between classwork and realworld applications.
Featured Profiles
The Power of Being Data Literate in a DataDriven World
Melissa Royo ‘09/’10 (applied statistics)
The applied nature of the statistics programs at RIT helped Melissa Royo ’09/’10 get a sense for how realworld data behaves.
Your Partners in Success: Meet Our Faculty, Dr. Wong
Dr. Tony Wong
Mathematics is a powerful tool for answering questions. From mitigating climate risks to splitting the dinner bill, Professor Wong shows students that math is more than just a prerequisite.
Curriculum for 20232024 for Applied Statistics and Data Analytics BS
Current Students: See Curriculum Requirements
Applied Statistics and Data Analytics, BS degree, typical course sequence
Course  Sem. Cr. Hrs.  

First Year  
ISCH110  Principles of Computing (General Education) This course is designed to introduce students to the central ideas of computing. Students will engage in activities that show how computing changes the world and impacts daily lives. Students will develop stepbystep written solutions to basic problems and implement their solutions using a programming language. Assignments will be completed both individually and in small teams. Students will be required to demonstrate oral and written communication skills through such assignments as short papers, homework, group discussions and debates, and development of a term paper. Computer Science majors may take this course only with department approval, and may not apply these credits toward their degree requirements. Lec/Lab 3 (Fall, Spring). 
3 
MATH181  Calculus I (General Education – Mathematical Perspective A) This is the first in a twocourse sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisites: MATH111 or (NMTH220 and NMTH260 or NMTH272 or NMTH275) or equivalent courses with a minimum grade of B, or a score of at least 60% on the RIT Mathematics Placement Exam.
Corequisites: MATH181R or equivalent course.) Lecture 6 (Fall, Spring). 
4 
MATH182  Calculus II (General Education – Mathematical Perspective B) This is the second in a twocourse sequence. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C or better in MATH181 or MATH181A or equivalent course.
Corequisites: MATH182R or equivalent course.) Lecture 6 (Fall, Spring). 
4 
MATH199  Mathematics and Statistics Seminar This course introduces the programs within the School of Mathematical Sciences, and provides an introduction to math and statistics software. The course provides practice in technical writing. Seminar 1 (Fall). 
1 
YOPS10  RIT 365: RIT Connections RIT 365 students participate in experiential learning opportunities designed to launch them into their career at RIT, support them in making multiple and varied connections across the university, and immerse them in processes of competency development. Students will plan for and reflect on their firstyear experiences, receive feedback, and develop a personal plan for future action in order to develop foundational selfawareness and recognize broadbased professional competencies. (This class is restricted to incoming 1st year or global campus students.) Lecture 1 (Fall, Spring). 
0 
General Education – Elective 
3  
General Education – FirstYear Writing (WI) 
3  
General Education – Ethical Perspective 
3  
General Education – Artistic Perspective 
3  
General Education – Natural Science Inquiry Perspective� 
4  
Second Year  
MATH200  Discrete Mathematics and Introduction to Proofs This course prepares students for professions that use mathematics in daily practice, and for mathematics courses beyond the introductory level where it is essential to communicate effectively in the language of mathematics. It covers various methods of mathematical proof, starting with basic techniques in propositional and predicate calculus and set theory, and then moving to applications in advanced mathematics. (Prerequisite: MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 3, Recitation 4 (Fall). 
3 
MATH251  Probability and Statistics This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to realworld problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH173 or MATH182 or MATH 182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). 
3 
STAT257  Statistical Inference Learn how data furthers understanding of science and engineering. This course covers basic statistical concepts, sampling theory, hypothesis testing, confidence intervals, point estimation, and simple linear regression. A statistical software package such as MINITAB will be used for data analysis and statistical applications. (Prerequisites: MATH251.
NOTE: Students cannot receive credit for both MATH252 and STAT257 nor for both STAT205 and STAT257.) Lecture 3 (Fall, Spring). 
3 
MATH399  Mathematical Sciences Job Search Seminar This course helps students prepare to search for coop or fulltime employment. Students will learn strategies for conducting a successful job search and transitioning into the work world. The course meets one hour each week for five weeks. Lecture 1 (Fall, Spring). 
0 
Choose one of the following:  4 

MATH221  Multivariable and Vector Calculus (General Education) This course is principally a study of the calculus of functions of two or more variables, but also includes a study of vectors, vectorvalued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, Stokes' Theorem, Green's Theorem, the Divergence Theorem, and applications in physics. Credit cannot be granted for both this course and MATH219. (Prerequisite: C or better MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). 

MATH221H  Honors Multivariable and Vector Calculus (General Education) This course is an honors version of MATH221. It includes an introduction to vectors, surfaces, and multivariable functions. It covers limits, partial derivatives and differentiability, multiple integrals, Stokes’ Theorem, Green’s Theorem, the Divergence Theorem, and applications. Unlike MATH221, students in this course will often be expected to learn elementary skills and concepts from their text so that inclass discussion can focus primarily on extending techniques, interpreting results, and exploring mathematical topics in greater depth; homework exercises and projects given in this class will require greater synthesis of concepts and skills, on average, than those in MATH221. Students earning credit for this course cannot earn credit for MATH219 or MATH221. (Prerequisites: C or better in MATH182 or MATH173 or MATH182A and Honors program status or at least a 3.2 cumulative GPA.) Lecture 4 (Fall). 

Choose one of the following:  3 

MATH241  Linear Algebra This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course. (Prerequisites: MATH190 or MATH200 or MATH219 or MATH220 or MATH221 or MATH221H or equivalent course.) Lecture 3 (Fall, Spring). 

MATH241H  Honors Linear Algebra This honors course introduces the basic concepts and techniques of linear algebra. Concepts are addressed at a higher level than the standard course in linear algebra, and the topic list is somewhat broader. Topics include linear independence and span, linear functions, solving systems of linear equations using Gaussian elimination, the arithmetic and algebra of matrices, basic properties and interpretation of determinants, vector spaces, the fundamental subspaces of a linear function, eigenvalues and eigenvectors, change of basis, similarity and diagonalization. Students will learn to communicate explanations of mathematical facts and techniques by participating in a collaborative workshop format, and will learn to use MATLAB to solve matrix equations. (Prerequisites: MATH219 or MATH221 or MATH221H or equivalent course and Honors program status or at least a 3.2 cumulative GPA.) Lecture 3 (Spring). 

Open Elective 
3  
General Education – Elective 
3  
General Education – Global Perspective 
3  
General Education – Social Perspective 
3  
General Education – Scientific Principles Perspective� 
4  
Third Year  
STAT305  Regression Analysis This course covers regression techniques with applications to the type of problems encountered in realworld situations. It includes use of the statistical software SAS. Topics include a review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, and various other models as time permits. (Prerequisites: MATH241 and (MATH252 or STAT257) or equivalent courses.) Lecture 3 (Spring). 
3 
STAT325  Design of Experiments (WIPR) This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include singlefactor analysis of variance, multiple comparisons and model validation, multifactor factorial designs, fixed, random and mixed models, expected mean square calculations, confounding, randomized block designs, and other designs and topics as time permits. (Prerequisites: STAT205 or MATH252 or STAT257 or equivalent course.) Lecture 3 (Fall). 
3 
Program Electives‡ 
15  
General Education – Immersion 1, 2 
6  
General Education – Elective 
3  
Fourth Year  
STAT405  Mathematical Statistics I This course provides a brief review of basic probability concepts and distribution theory. It covers mathematical properties of distributions needed for statistical inference. (Prerequisites: STAT205 or MATH252 or STAT257 or equivalent course.) Lecture 3 (Fall). 
3 
STAT406  Mathematical Statistics II This course is a continuation of STAT405 covering classical and Bayesian methods in estimation theory, chisquare test, NeymanPearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT405 or equivalent course.) Lecture 3 (Spring). 
3 
STAT500  Senior Capstone in Statistics (WIPR) This course introduces the student to statistical situations not encountered in regular course of study. It integrates and synthesizes concepts in statistical theory with applications. Topics include openended analysis of data, current techniques and practice of statistics, development of statistical communication skills and the use of statistical software tools in data analysis. Each student is required to learn and use a statistical technique beyond what is covered in the previous courses. Students are expected to introduce the method in a presentation and to prepare a comprehensive, professional report detailing the statistical method and its application to a data set. (Corequisites: STAT305 and STAT325 or equivalent courses.) Lecture 3 (Spring). 
3 
STAT501  Experiential Learning Requirement in Statistics The experiential learning (EL) requirement may be fulfilled through a variety of methods including capstone, coop, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. All experiences must be approved by the School of Mathematical Sciences. Successful completion of the required elements will result in a grade of S in this course. Lecture (Fall, Spring, Summer). 
0 
General Education – Immersion 3 
3  
Program Electives‡ 
3  
Open Electives 
9  
General Education – Electives 
6  
Total Semester Credit Hours  120 
Please see General Education Curriculum (GE) for more information.
(WI) Refers to a writing intensive course within the major.
* Please see Wellness Education Requirement for more information. Students completing bachelor's degrees are required to complete two different Wellness courses.
† Students will satisfy this requirement by taking either University Physics I (PHYS211) and University Physics II (PHYS212) or General & Analytical Chemistry I and Lab (CHMG141/145) and General & Analytical Chemistry II and Lab (CHMG142/146) or General Biology I and Lab (BIOL101/103) and General Biology II and Lab (BIOL102/104).
‡ Three of the six program electives must be from the following list of courses: Actuarial Mathematics (MATH255), Topics in the Mathematics of Finance (MATH261), Stochastic Processes (MATH505), Introduction to Time Series (STAT335), Nonparametric Statistics (STAT345), Statistical Software  R (STAT511), Statistical Quality Control (STAT521), Data Mining (STAT547), Survey Design and Analysis (STAT572), Categorical Data Analysis (STAT584). A program elective is any MATH or STAT course with a course number higher than 250.
Up to 2 program electives can be selected from the following list: Financial Accounting (ACCT110), Data Literacy, Analytics and Decision Making (BANA255), Statistical Analysis for Bioinformatics (BIOL470), Operations Management (DECS310), Econometrics I (ECON403), Financial Management (FINC220), Financial Analytics (FINC580), Lean Six Sigma Fundamentals ISEE582), Marketing Analytics (MKTG365)
Combined Accelerated Bachelor's/Master's Degrees
The curriculum below outlines the typical course sequence(s) for combined accelerated degrees available with this bachelor's degree.
Applied Statistics and Data Analytics, BS degree/Applied and Computational Mathematics (thesis option), MS degree, typical course sequence
Course  Sem. Cr. Hrs.  

First Year  
ISCH110  Principles of Computing This course is designed to introduce students to the central ideas of computing. Students will engage in activities that show how computing changes the world and impacts daily lives. Students will develop stepbystep written solutions to basic problems and implement their solutions using a programming language. Assignments will be completed both individually and in small teams. Students will be required to demonstrate oral and written communication skills through such assignments as short papers, homework, group discussions and debates, and development of a term paper. Computer Science majors may take this course only with department approval, and may not apply these credits toward their degree requirements. Lec/Lab 3 (Fall, Spring). 
3 
MATH181  General Education  Mathematical Perspective A: ProjectBased Calculus I This is the first in a twocourse sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisites: MATH111 or (NMTH220 and NMTH260 or NMTH272 or NMTH275) or equivalent courses with a minimum grade of B, or a score of at least 60% on the RIT Mathematics Placement Exam.
Corequisites: MATH181R or equivalent course.) Lecture 6 (Fall, Spring). 
4 
MATH182  General Education  Mathematical Perspective B: ProjectBased Calculus II This is the second in a twocourse sequence. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C or better in MATH181 or MATH181A or equivalent course.
Corequisites: MATH182R or equivalent course.) Lecture 6 (Fall, Spring). 
4 
MATH199  Mathematics and Statistics Seminar This course introduces the programs within the School of Mathematical Sciences, and provides an introduction to math and statistics software. The course provides practice in technical writing. Seminar 1 (Fall). 
1 
YOPS10  RIT 365: RIT Connections RIT 365 students participate in experiential learning opportunities designed to launch them into their career at RIT, support them in making multiple and varied connections across the university, and immerse them in processes of competency development. Students will plan for and reflect on their firstyear experiences, receive feedback, and develop a personal plan for future action in order to develop foundational selfawareness and recognize broadbased professional competencies. (This class is restricted to incoming 1st year or global campus students.) Lecture 1 (Fall, Spring). 
0 
General Education  Elective 
3  
General Education  First Year Writing (WI) 
3  
General Education  Ethical Perspective 
3  
General Education  Artistic Perspective 
3  
General Education  Natural Science Inquiry Perspective� 
4  
General Education  Scientific Principles Perspective� 
4  
Second Year  
MATH200  Discrete Mathematics and Introduction to Proofs This course prepares students for professions that use mathematics in daily practice, and for mathematics courses beyond the introductory level where it is essential to communicate effectively in the language of mathematics. It covers various methods of mathematical proof, starting with basic techniques in propositional and predicate calculus and set theory, and then moving to applications in advanced mathematics. (Prerequisite: MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 3, Recitation 4 (Fall). 
3 
MATH231  Differential Equations This course is an introduction to the study of ordinary differential equations and their applications. Topics include solutions to first order equations and linear second order equations, method of undetermined coefficients, variation of parameters, linear independence and the Wronskian, vibrating systems, and Laplace transforms. (Prerequisite: MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). 
3 
MATH251  Probability and Statistics This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to realworld problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH173 or MATH182 or MATH 182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). 
3 
MATH399  Mathematical Science Job Search Seminar This course helps students prepare to search for coop or fulltime employment. Students will learn strategies for conducting a successful job search and transitioning into the work world. The course meets one hour each week for five weeks. Lecture 1 (Fall, Spring). 
0 
Choose one of the following:  4 

MATH221  Multivariable and Vector Calculus This course is principally a study of the calculus of functions of two or more variables, but also includes a study of vectors, vectorvalued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, Stokes' Theorem, Green's Theorem, the Divergence Theorem, and applications in physics. Credit cannot be granted for both this course and MATH219. (Prerequisite: C or better MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). 

MATH221H  Honors Multivariable and Vector Calculus This course is an honors version of MATH221. It includes an introduction to vectors, surfaces, and multivariable functions. It covers limits, partial derivatives and differentiability, multiple integrals, Stokes’ Theorem, Green’s Theorem, the Divergence Theorem, and applications. Unlike MATH221, students in this course will often be expected to learn elementary skills and concepts from their text so that inclass discussion can focus primarily on extending techniques, interpreting results, and exploring mathematical topics in greater depth; homework exercises and projects given in this class will require greater synthesis of concepts and skills, on average, than those in MATH221. Students earning credit for this course cannot earn credit for MATH219 or MATH221. (Prerequisites: C or better in MATH182 or MATH173 or MATH182A and Honors program status or at least a 3.2 cumulative GPA.) Lecture 4 (Fall). 

Choose one of the following:  3 

MATH241  Linear Algebra This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course. (Prerequisites: MATH190 or MATH200 or MATH219 or MATH220 or MATH221 or MATH221H or equivalent course.) Lecture 3 (Fall, Spring). 

MATH241H  Honors Linear Algebra This honors course introduces the basic concepts and techniques of linear algebra. Concepts are addressed at a higher level than the standard course in linear algebra, and the topic list is somewhat broader. Topics include linear independence and span, linear functions, solving systems of linear equations using Gaussian elimination, the arithmetic and algebra of matrices, basic properties and interpretation of determinants, vector spaces, the fundamental subspaces of a linear function, eigenvalues and eigenvectors, change of basis, similarity and diagonalization. Students will learn to communicate explanations of mathematical facts and techniques by participating in a collaborative workshop format, and will learn to use MATLAB to solve matrix equations. (Prerequisites: MATH219 or MATH221 or MATH221H or equivalent course and Honors program status or at least a 3.2 cumulative GPA.) Lecture 3 (Spring). 

STAT257  Statistical Inference Learn how data furthers understanding of science and engineering. This course covers basic statistical concepts, sampling theory, hypothesis testing, confidence intervals, point estimation, and simple linear regression. A statistical software package such as MINITAB will be used for data analysis and statistical applications. (Prerequisites: MATH251.
NOTE: Students cannot receive credit for both MATH252 and STAT257 nor for both STAT205 and STAT257.) Lecture 3 (Fall, Spring). 
3 
General Education  Immersion 1, 2 
6  
General Education  Global Perspective 
3  
General Education  Social Perspective 
3  
Third Year  
STAT305  Regression Analysis This course covers regression techniques with applications to the type of problems encountered in realworld situations. It includes use of the statistical software SAS. Topics include a review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, and various other models as time permits. (Prerequisites: MATH241 and (MATH252 or STAT257) or equivalent courses.) Lecture 3 (Spring). 
3 
STAT325  Design of Experiments (WIPR) This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include singlefactor analysis of variance, multiple comparisons and model validation, multifactor factorial designs, fixed, random and mixed models, expected mean square calculations, confounding, randomized block designs, and other designs and topics as time permits. (Prerequisites: STAT205 or MATH252 or STAT257 or equivalent course.) Lecture 3 (Fall). 
3 
Open Electives 
9  
General Education  Immersion 3 
3  
Program Electives‡ 
12  
Fourth Year  
MATH606  Graduate Seminar I The course prepares students to engage in activities necessary for independent mathematical research and introduces students to a broad range of active interdisciplinary programs related to applied mathematics. (This course is restricted to students in the ACMTHMS or MATHMLPHD programs.) Lecture 2 (Fall). 
1 
MATH607  Graduate Seminar II This course is a continuation of Graduate Seminar I. It prepares students to engage in activities necessary for independent mathematical research and introduces them to a broad range of active interdisciplinary programs related to applied mathematics. (Prerequisite: MATH606 or equivalent course or students in the ACMTHMS or MATHMLPHD programs.) Lecture 2 (Spring). 
1 
STAT405  Mathematical Statistics I This course provides a brief review of basic probability concepts and distribution theory. It covers mathematical properties of distributions needed for statistical inference. (Prerequisites: STAT205 or MATH252 or STAT257 or equivalent course.) Lecture 3 (Fall). 
3 
STAT406  Mathematical Statistics II This course is a continuation of STAT405 covering classical and Bayesian methods in estimation theory, chisquare test, NeymanPearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT405 or equivalent course.) Lecture 3 (Spring). 
3 
STAT500  Senior Capstone in Statistics (WIPR) This course introduces the student to statistical situations not encountered in regular course of study. It integrates and synthesizes concepts in statistical theory with applications. Topics include openended analysis of data, current techniques and practice of statistics, development of statistical communication skills and the use of statistical software tools in data analysis. Each student is required to learn and use a statistical technique beyond what is covered in the previous courses. Students are expected to introduce the method in a presentation and to prepare a comprehensive, professional report detailing the statistical method and its application to a data set. (Corequisites: STAT305 and STAT325 or equivalent courses.) Lecture 3 (Spring). 
3 
STAT501  Experiential Learning Requirement in Statistics The experiential learning (EL) requirement may be fulfilled through a variety of methods including capstone, coop, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. All experiences must be approved by the School of Mathematical Sciences. Successful completion of the required elements will result in a grade of S in this course. Lecture (Fall, Spring, Summer). 
0 
Math Graduate Core Courses 
9  
General Education  Electives 
9  
Open Elective 
3  
Fifth Year  
MATH790  Research & Thesis Masterslevel research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course is restricted to students in the ACMTHMS or MATHMLPHD programs.) Thesis (Fall, Spring, Summer). 
7 
Math Graduate Electives 
12  
Total Semester Credit Hours  144 
Please see General Education Curriculum for more information.
(WI) Refers to a writing intensive course within the major.
* Please see Wellness Education Requirement for more information. Students completing bachelor's degrees are required to complete two different Wellness courses.
† Students will satisfy this requirement by taking either University Physics I (PHYS211) and University Physics II (PHYS212) or General & Analytical Chemistry I and Lab (CHMG141/145) and General & Analytical Chemistry II and Lab (CHMG142/146) or General Biology I and Lab (BIOL101/103) and General Biology II and Lab (BIOL102/104).
‡ The four program electives must be from the following list of courses: Actuarial Mathematics (MATH255), Topics in Mathematics of Finance (MATH261), Stochastic Processes (MATH505), Introduction to Time Series (STAT335), Nonparametric Statistics (STAT345), Multivariate Analysis (STAT425), Statistical Software  R (STAT511), Statistical Quality Control (STAT521), Data Mining (STAT547), Survey Design and Analysis (STAT572), Categorical Data Analysis (STAT584). A program elective is any MATH or STAT course with a course number higher than 250.
Applied Statistics and Data Analytics, BS degree/Applied and Computational Mathematics (project option), MS degree, typical course sequence
Course  Sem. Cr. Hrs.  

First Year  
ISCH110  Principles of Computing This course is designed to introduce students to the central ideas of computing. Students will engage in activities that show how computing changes the world and impacts daily lives. Students will develop stepbystep written solutions to basic problems and implement their solutions using a programming language. Assignments will be completed both individually and in small teams. Students will be required to demonstrate oral and written communication skills through such assignments as short papers, homework, group discussions and debates, and development of a term paper. Computer Science majors may take this course only with department approval, and may not apply these credits toward their degree requirements. Lec/Lab 3 (Fall, Spring). 
3 
MATH181  General Education  Mathematical Perspective A: ProjectBased Calculus I This is the first in a twocourse sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisites: MATH111 or (NMTH220 and NMTH260 or NMTH272 or NMTH275) or equivalent courses with a minimum grade of B, or a score of at least 60% on the RIT Mathematics Placement Exam.
Corequisites: MATH181R or equivalent course.) Lecture 6 (Fall, Spring). 
4 
MATH182  General Education  Mathematical Perspective B: ProjectBased Calculus II This is the second in a twocourse sequence. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C or better in MATH181 or MATH181A or equivalent course.
Corequisites: MATH182R or equivalent course.) Lecture 6 (Fall, Spring). 
4 
MATH199  Mathematics and Statistics Seminar This course introduces the programs within the School of Mathematical Sciences, and provides an introduction to math and statistics software. The course provides practice in technical writing. Seminar 1 (Fall). 
1 
YOPS10  RIT 365: RIT Connections RIT 365 students participate in experiential learning opportunities designed to launch them into their career at RIT, support them in making multiple and varied connections across the university, and immerse them in processes of competency development. Students will plan for and reflect on their firstyear experiences, receive feedback, and develop a personal plan for future action in order to develop foundational selfawareness and recognize broadbased professional competencies. (This class is restricted to incoming 1st year or global campus students.) Lecture 1 (Fall, Spring). 
0 
General Education  Elective 
3  
General Education  First Year Writing (WI) 
3  
General Education  Artistic Perspective 
3  
General Education  Ethical Perspective 
3  
General Education  Natural Science Inquiry Perspective� 
4  
General Education  Scientific Principles Perspective� 
4  
Second Year  
MATH200  Discrete Mathematics and Introduction to Proofs This course prepares students for professions that use mathematics in daily practice, and for mathematics courses beyond the introductory level where it is essential to communicate effectively in the language of mathematics. It covers various methods of mathematical proof, starting with basic techniques in propositional and predicate calculus and set theory, and then moving to applications in advanced mathematics. (Prerequisite: MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 3, Recitation 4 (Fall). 
3 
Choose one of the following:  4 

MATH221  Multivariable and Vector Calculus This course is principally a study of the calculus of functions of two or more variables, but also includes a study of vectors, vectorvalued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, Stokes' Theorem, Green's Theorem, the Divergence Theorem, and applications in physics. Credit cannot be granted for both this course and MATH219. (Prerequisite: C or better MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). 

MATH221H  Honors Multivariable and Vector Calculus This course is an honors version of MATH221. It includes an introduction to vectors, surfaces, and multivariable functions. It covers limits, partial derivatives and differentiability, multiple integrals, Stokes’ Theorem, Green’s Theorem, the Divergence Theorem, and applications. Unlike MATH221, students in this course will often be expected to learn elementary skills and concepts from their text so that inclass discussion can focus primarily on extending techniques, interpreting results, and exploring mathematical topics in greater depth; homework exercises and projects given in this class will require greater synthesis of concepts and skills, on average, than those in MATH221. Students earning credit for this course cannot earn credit for MATH219 or MATH221. (Prerequisites: C or better in MATH182 or MATH173 or MATH182A and Honors program status or at least a 3.2 cumulative GPA.) Lecture 4 (Fall). 

MATH231  Differential Equations This course is an introduction to the study of ordinary differential equations and their applications. Topics include solutions to first order equations and linear second order equations, method of undetermined coefficients, variation of parameters, linear independence and the Wronskian, vibrating systems, and Laplace transforms. (Prerequisite: MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). 
3 
Choose one of the following:  3 

MATH241  Linear Algebra This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course. (Prerequisites: MATH190 or MATH200 or MATH219 or MATH220 or MATH221 or MATH221H or equivalent course.) Lecture 3 (Fall, Spring). 

MATH241H  Honor Linear Algebra This honors course introduces the basic concepts and techniques of linear algebra. Concepts are addressed at a higher level than the standard course in linear algebra, and the topic list is somewhat broader. Topics include linear independence and span, linear functions, solving systems of linear equations using Gaussian elimination, the arithmetic and algebra of matrices, basic properties and interpretation of determinants, vector spaces, the fundamental subspaces of a linear function, eigenvalues and eigenvectors, change of basis, similarity and diagonalization. Students will learn to communicate explanations of mathematical facts and techniques by participating in a collaborative workshop format, and will learn to use MATLAB to solve matrix equations. (Prerequisites: MATH219 or MATH221 or MATH221H or equivalent course and Honors program status or at least a 3.2 cumulative GPA.) Lecture 3 (Spring). 

MATH251  Probability and Statistics I This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to realworld problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH173 or MATH182 or MATH 182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). 
3 
MATH399  Mathematical Science Job Search Seminar This course helps students prepare to search for coop or fulltime employment. Students will learn strategies for conducting a successful job search and transitioning into the work world. The course meets one hour each week for five weeks. Lecture 1 (Fall, Spring). 
0 
STAT257  Statistical Inference Learn how data furthers understanding of science and engineering. This course covers basic statistical concepts, sampling theory, hypothesis testing, confidence intervals, point estimation, and simple linear regression. A statistical software package such as MINITAB will be used for data analysis and statistical applications. (Prerequisites: MATH251.
NOTE: Students cannot receive credit for both MATH252 and STAT257 nor for both STAT205 and STAT257.) Lecture 3 (Fall, Spring). 
3 
General Education  Immersion 1, 2 
6  
General Education  Global Perspective 
3  
General Education  Social Perspective 
3  
Third Year  
STAT305  Regression Analysis This course covers regression techniques with applications to the type of problems encountered in realworld situations. It includes use of the statistical software SAS. Topics include a review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, and various other models as time permits. (Prerequisites: MATH241 and (MATH252 or STAT257) or equivalent courses.) Lecture 3 (Spring). 
3 
STAT325  Design of Experiments (WIPR) This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include singlefactor analysis of variance, multiple comparisons and model validation, multifactor factorial designs, fixed, random and mixed models, expected mean square calculations, confounding, randomized block designs, and other designs and topics as time permits. (Prerequisites: STAT205 or MATH252 or STAT257 or equivalent course.) Lecture 3 (Fall). 
3 
Open Electives 
9  
General Education  Immersion 3 
3  
Program Electives‡ 
12  
Fourth Year  
MATH606  Graduate Seminar I The course prepares students to engage in activities necessary for independent mathematical research and introduces students to a broad range of active interdisciplinary programs related to applied mathematics. (This course is restricted to students in the ACMTHMS or MATHMLPHD programs.) Lecture 2 (Fall). 
1 
MATH607  Graduate Seminar II This course is a continuation of Graduate Seminar I. It prepares students to engage in activities necessary for independent mathematical research and introduces them to a broad range of active interdisciplinary programs related to applied mathematics. (Prerequisite: MATH606 or equivalent course or students in the ACMTHMS or MATHMLPHD programs.) Lecture 2 (Spring). 
1 
STAT405  Mathematical Statistics I This course provides a brief review of basic probability concepts and distribution theory. It covers mathematical properties of distributions needed for statistical inference. (Prerequisites: STAT205 or MATH252 or STAT257 or equivalent course.) Lecture 3 (Fall). 
3 
STAT406  Mathematical Statistics II This course is a continuation of STAT405 covering classical and Bayesian methods in estimation theory, chisquare test, NeymanPearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT405 or equivalent course.) Lecture 3 (Spring). 
3 
STAT500  Senior Capstone in Statistics (WIPR) This course introduces the student to statistical situations not encountered in regular course of study. It integrates and synthesizes concepts in statistical theory with applications. Topics include openended analysis of data, current techniques and practice of statistics, development of statistical communication skills and the use of statistical software tools in data analysis. Each student is required to learn and use a statistical technique beyond what is covered in the previous courses. Students are expected to introduce the method in a presentation and to prepare a comprehensive, professional report detailing the statistical method and its application to a data set. (Corequisites: STAT305 and STAT325 or equivalent courses.) Lecture 3 (Spring). 
3 
STAT501  Experiential Learning Requirement in Statistics The experiential learning (EL) requirement may be fulfilled through a variety of methods including capstone, coop, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. All experiences must be approved by the School of Mathematical Sciences. Successful completion of the required elements will result in a grade of S in this course. Lecture (Fall, Spring, Summer). 
0 
Math Graduate Core Courses 
9  
General Education  Electives 
9  
Open Elective 
3  
Fifth Year  
MATH790  Research & Thesis Masterslevel research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course is restricted to students in the ACMTHMS or MATHMLPHD programs.) Thesis (Fall, Spring, Summer). 
4 
Graduate Electives 
15  
Total Semester Credit Hours  144 
Please see General Education Curriculum for more information.
(WI) Refers to a writing intensive course within the major.
* Please see Wellness Education Requirement for more information. Students completing bachelor's degrees are required to complete two different Wellness courses.
† Students will satisfy this requirement by taking either University Physics I (PHYS211) and University Physics II (PHYS212) or General & Analytical Chemistry I and Lab (CHMG141/145) and General & Analytical Chemistry II and Lab (CHMG142/146) or General Biology I and Lab (BIOL101/103) and General Biology II and Lab (BIOL102/104).
‡ The four program electives must be from the following list of courses: Actuarial Mathematics (MATH255), Topics in Mathematics of Finance (MATH261), Stochastic Processes (MATH505), Introduction to Time Series (STAT335), Nonparametric Statistics (STAT345), Multivariate Analysis (STAT425), Statistical Software  R (STAT511), Statistical Quality Control (STAT521), Data Mining (STAT547), Survey Design and Analysis (STAT572), Categorical Data Analysis (STAT584). A program elective is any MATH or STAT course with a course number higher than 250.
Applied Statistics and Data Analytics, BS degree/Applied Statistics, MS degree, typical course sequence
Course  Sem. Cr. Hrs.  

First Year  
ISCH110  Principles of Computing This course is designed to introduce students to the central ideas of computing. Students will engage in activities that show how computing changes the world and impacts daily lives. Students will develop stepbystep written solutions to basic problems and implement their solutions using a programming language. Assignments will be completed both individually and in small teams. Students will be required to demonstrate oral and written communication skills through such assignments as short papers, homework, group discussions and debates, and development of a term paper. Computer Science majors may take this course only with department approval, and may not apply these credits toward their degree requirements. Lec/Lab 3 (Fall, Spring). 
3 
MATH181  General Education – Mathematical Perspective A: ProjectBased Calculus I This is the first in a twocourse sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisites: MATH111 or (NMTH220 and NMTH260 or NMTH272 or NMTH275) or equivalent courses with a minimum grade of B, or a score of at least 60% on the RIT Mathematics Placement Exam.
Corequisites: MATH181R or equivalent course.) Lecture 6 (Fall, Spring). 
4 
MATH182  General Education – Mathematical Perspective B: ProjectBased Calculus II This is the second in a twocourse sequence. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C or better in MATH181 or MATH181A or equivalent course.
Corequisites: MATH182R or equivalent course.) Lecture 6 (Fall, Spring). 
4 
MATH199  Mathematics and Statistics Seminar I This course introduces the programs within the School of Mathematical Sciences, and provides an introduction to math and statistics software. The course provides practice in technical writing. Seminar 1 (Fall). 
1 
YOPS10  RIT 365: RIT Connections RIT 365 students participate in experiential learning opportunities designed to launch them into their career at RIT, support them in making multiple and varied connections across the university, and immerse them in processes of competency development. Students will plan for and reflect on their firstyear experiences, receive feedback, and develop a personal plan for future action in order to develop foundational selfawareness and recognize broadbased professional competencies. (This class is restricted to incoming 1st year or global campus students.) Lecture 1 (Fall, Spring). 
0 
General Education – Elective 
3  
General Education – FirstYear Writing (WI) 
3  
General Education – Ethical Perspective 
3  
General Education – Artistic Perspective 
3  
General Education – Natural Science Inquiry Perspective� 
4  
General Education – Scientific Principles Perspective� 
4  
Second Year  
MATH200  Discrete Mathematics and Introduction to Proofs This course prepares students for professions that use mathematics in daily practice, and for mathematics courses beyond the introductory level where it is essential to communicate effectively in the language of mathematics. It covers various methods of mathematical proof, starting with basic techniques in propositional and predicate calculus and set theory, and then moving to applications in advanced mathematics. (Prerequisite: MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 3, Recitation 4 (Fall). 
3 
MATH251  Probability and Statistics This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to realworld problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH173 or MATH182 or MATH 182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). 
3 
MATH399  Mathematical Science Job Search Seminar This course helps students prepare to search for coop or fulltime employment. Students will learn strategies for conducting a successful job search and transitioning into the work world. The course meets one hour each week for five weeks. Lecture 1 (Fall, Spring). 
0 
STAT257  Statistical Inference Learn how data furthers understanding of science and engineering. This course covers basic statistical concepts, sampling theory, hypothesis testing, confidence intervals, point estimation, and simple linear regression. A statistical software package such as MINITAB will be used for data analysis and statistical applications. (Prerequisites: MATH251.
NOTE: Students cannot receive credit for both MATH252 and STAT257 nor for both STAT205 and STAT257.) Lecture 3 (Fall, Spring). 
3 
Choose one of the following:  4 

MATH221  General Education – Elective: Multivariable and Vector Calculus This course is principally a study of the calculus of functions of two or more variables, but also includes a study of vectors, vectorvalued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, Stokes' Theorem, Green's Theorem, the Divergence Theorem, and applications in physics. Credit cannot be granted for both this course and MATH219. (Prerequisite: C or better MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). 

MATH221H  General Education – Elective: Honors Multivariable and Vector Calculus This course is an honors version of MATH221. It includes an introduction to vectors, surfaces, and multivariable functions. It covers limits, partial derivatives and differentiability, multiple integrals, Stokes’ Theorem, Green’s Theorem, the Divergence Theorem, and applications. Unlike MATH221, students in this course will often be expected to learn elementary skills and concepts from their text so that inclass discussion can focus primarily on extending techniques, interpreting results, and exploring mathematical topics in greater depth; homework exercises and projects given in this class will require greater synthesis of concepts and skills, on average, than those in MATH221. Students earning credit for this course cannot earn credit for MATH219 or MATH221. (Prerequisites: C or better in MATH182 or MATH173 or MATH182A and Honors program status or at least a 3.2 cumulative GPA.) Lecture 4 (Fall). 

Choose one of the following:  3 

MATH241  Linear Algebra This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course. (Prerequisites: MATH190 or MATH200 or MATH219 or MATH220 or MATH221 or MATH221H or equivalent course.) Lecture 3 (Fall, Spring). 

MATH241H  Honors Linear Algebra This honors course introduces the basic concepts and techniques of linear algebra. Concepts are addressed at a higher level than the standard course in linear algebra, and the topic list is somewhat broader. Topics include linear independence and span, linear functions, solving systems of linear equations using Gaussian elimination, the arithmetic and algebra of matrices, basic properties and interpretation of determinants, vector spaces, the fundamental subspaces of a linear function, eigenvalues and eigenvectors, change of basis, similarity and diagonalization. Students will learn to communicate explanations of mathematical facts and techniques by participating in a collaborative workshop format, and will learn to use MATLAB to solve matrix equations. (Prerequisites: MATH219 or MATH221 or MATH221H or equivalent course and Honors program status or at least a 3.2 cumulative GPA.) Lecture 3 (Spring). 

General Education – Global Perspective 
3  
General Education – Social Perspective 
3  
General Education – Elective 
3  
General Education  Immersion 1 
3  
Open Elective 
3  
Third Year  
STAT641  Applied Linear Models  Regression A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building. (This class is restricted to students in the APPSTATMS, SMPPIACT, or APPSTATU programs.) Lecture 3 (Fall, Spring, Summer). 
3 
STAT642  Applied Linear Models  ANOVA This course introduces students to analysis of models with categorical factors, with emphasis on interpretation. Topics include the role of statistics in scientific studies, fixed and random effects, mixed models, covariates, hierarchical models, and repeated measures. (This class is restricted to students in the APPSTATMS, SMPPIACT, or APPSTATU programs.) Lecture 3 (Spring, Summer). 
3 
General Education – Immersion 2,3 
6  
General Education – Electives 
6  
Program Electives‡ 
12  
Fourth Year  
STAT405  Mathematical Statistics I This course provides a brief review of basic probability concepts and distribution theory. It covers mathematical properties of distributions needed for statistical inference. (Prerequisites: STAT205 or MATH252 or STAT257 or equivalent course.) Lecture 3 (Fall). 
3 
STAT406  Mathematical Statistics II This course is a continuation of STAT405 covering classical and Bayesian methods in estimation theory, chisquare test, NeymanPearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT405 or equivalent course.) Lecture 3 (Spring). 
3 
STAT500  Senior Capstone in Statistics (WIPR) This course introduces the student to statistical situations not encountered in regular course of study. It integrates and synthesizes concepts in statistical theory with applications. Topics include openended analysis of data, current techniques and practice of statistics, development of statistical communication skills and the use of statistical software tools in data analysis. Each student is required to learn and use a statistical technique beyond what is covered in the previous courses. Students are expected to introduce the method in a presentation and to prepare a comprehensive, professional report detailing the statistical method and its application to a data set. (Corequisites: STAT305 and STAT325 or equivalent courses.) Lecture 3 (Spring). 
3 
STAT501  Experiential Learning Requirement in Statistics The experiential learning (EL) requirement may be fulfilled through a variety of methods including capstone, coop, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. All experiences must be approved by the School of Mathematical Sciences. Successful completion of the required elements will result in a grade of S in this course. Lecture (Fall, Spring, Summer). 
0 
Program Electives‡ 
6  
Statistics Graduate Elective 
3  
General Education – Electives 
3  
Open Electives 
9  
Fifth Year  
STAT631  Foundations of Statistics This course introduces principles of probability and statistics with a strong emphasis on conceptual aspects of statistical inference. Topics include fundamentals of probability, probability distribution functions, expectation and variance, discrete and continuous distributions, sampling distributions, confidence intervals and hypothesis tests. (This course is restricted to students in APPSTATMS or SMPPIACT.) Lecture 3 (Fall, Spring). 
3 
STAT790  Capstone Thesis/Project This course is a graduate course for students enrolled in the Thesis/Project track of the MS Applied Statistics Program. (Enrollment in this course requires permission from the Director of Graduate Programs for Applied Statistics.) (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer). 
3 
Statistics Graduate Electives 
15  
Total Semester Credit Hours  144 
Please see General Education Curriculum (GE) for more information.
(WI) Refers to a writing intensive course within the major.
* Please see Wellness Education Requirement for more information. Students completing bachelor's degrees are required to complete two different Wellness courses.
† Students will satisfy this requirement by taking either University Physics I (PHYS211) and University Physics II (PHYS212) or General & Analytical Chemistry I and Lab (CHMG141/145) and General & Analytical Chemistry II and Lab (CHMG142/146) or General Biology I and Lab (BIOL101/103) and General Biology II and Lab (BIOL102/104).
‡ Three of the six program electives must be from the following list of courses: Actuarial Mathematics (MATH255), Topics in Mathematics of Finance (MATH261), Stochastic Processes (MATH505), Introduction to Time Series (STAT335), Nonparametric Statistics (STAT345), Multivariate Analysis (STAT425), Statistical Software  R (STAT511), Statistical Quality Control (STAT521), Data Mining (STAT547), Survey Design and Analysis (STAT572), Categorical Data Analysis (STAT584). A program elective is any MATH or STAT course with a course number higher than 250.
Admissions and Financial Aid
This program is STEM designated when studying on campus and full time.
FirstYear Admission
A strong performance in a college preparatory program is expected. This includes:
 4 years of English
 3 years of social studies and/or history
 4 years of mathematics is required and must include algebra, geometry, algebra 2/trigonometry, and precalculus. Calculus is preferred.
 23 years of science is required and must include chemistry or physics; both are recommended.
Transfer Admission
Transfer course recommendations without associate degree
Courses in liberal arts, physics, math, and chemistry
Appropriate associate degree programs for transfer
AS degree in liberal arts with math/science option
Financial Aid and Scholarships
100% of all incoming firstyear and transfer students receive aid.
RIT’s personalized and comprehensive financial aid program includes scholarships, grants, loans, and campus employment programs. When all these are put to work, your actual cost may be much lower than the published estimated cost of attendance.
Learn more about financial aid and scholarships
Research
Undergraduate Research Opportunities
Many students join research teams and engage in research projects starting as early as their first year. Participation in undergraduate research leads to the development of realworld skills, enhanced problemsolving techniques, and broader career opportunities. Our students have opportunities to travel to national conferences for presentations and also become contributing authors on peerreviewed manuscripts. Explore the variety of mathematics and statistics undergraduate research projects happening across the university.
Latest News

April 16, 2024
RIT’s Expressive Communication Center students explore ableism in communicationfocused settings
RIT's Expressive Communication Center delves into the challenges of ableism in communication education, presenting their findings at a national conference in Arizona. ECC students aim to foster dialogue on accessibility and inclusive communication practices, advocating for more inclusive assessment methods in higher education.

September 1, 2022
Scientists find the social cost of carbon is more than triple the current federal estimate
After years of robust modeling and analysis, a multiinstitutional team including researchers from RIT has released an updated social cost of carbon estimate that reflects new methodologies and key scientific advancements.

December 8, 2021
Setting the Stage for the Performing Academic
RIT students have never had as many ways to pursue their love of performing arts than they do now. From scholarships, new clubs and classes, private music lessons, community partnerships, and exciting new venues being built on campus, performing arts for RIT students is literally becoming a show stopper.