Applied Statistics and Data Analytics Bachelor of Science Degree
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.
$77K
Median First-Year 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
Career Connections: Network with recruiters from National Labs and federally-funded Research Centers to explore co-op, internship, research, and full-time employment opportunities.
Gain Work Experience: Complete a co-op or internship, engage in undergraduate research, or study abroad to gain real-world experience.
Jobs at Industry Leading Companies: Recent graduates are employed at Freehold Capital Management, Qool Media, Excellus BlueCross BlueShield, 3M, and General Electric.
A Robust Community: Join PiRIT, a student club that fosters a community of students and faculty in mathematics and statistics.
Accelerated Bachelor’s/Master’s Available: Earn both your bachelor’s and your master’s in less time and with a cost savings, giving you a competitive advantage in your field.
STEM-OPT Visa Eligible: The STEM Optional Practical Training (OPT) program allows full-time, on-campus international students on an F-1 student visa to stay and work in the U.S. for up to three years after graduation.
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.
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
Early courses in the statistics bachelor's degree 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 cutting-edge 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 real-world 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 hands-on experience that comes from co-ops, internships, research, study abroad, and more.
- Applied Statistics and Data Analytics BS/Applied and Computational Mathematics MS:
Combine your applied statistics and data analytics BS degree with a master’s in applied and computational mathematics to obtain a deep knowledge of mathematical and statistical analysis that employers are looking for. These complementary programs prepare graduates for careers in a broad spectrum of industries from health care to insurance to communications and beyond. Get the background you need in mathematics and statistics coupled with the applied training that will set you apart in computing, modeling, and analysis to launch a career in an industry that excites you. - Applied Statistics and Data Analytics BS/Applied Statistics MS:
Become a statistics professional with this combined accelerated dual degree. You’ll study statistical methodology, applications, and computing that you can apply to an industry that interests you such as insurance, government, health care, and more. With employment opportunities for statisticians continuing to grow, graduates of this degree pathway go on to excellent job placements with great starting salaries at companies like Excellus, Regeneron Pharmaceuticals, L3Harris, and Capital One, just to name a few - +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:
-
Next Steps to Enroll
Accept your offer of admission and take the next steps toward becoming an RIT Tiger.
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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
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 real-world career experience that sets you apart. It’s exposure–early and often–to a variety of professional work environments, career paths, and industries.
Co-ops and internships take your knowledge and turn it into know-how. Experiential learning opportunities in statistics include a range of hands-on experiences, from co-ops and internships to undergraduate research that enable you to apply your statistical knowledge in professional settings while you make valuable connections between classwork and real-world applications.
Featured Work and Profiles
-
An International Student’s Path from RIT to the United Nations
An international student from Mexico, Alethia Jimenez ’06 used her RIT education to launch a career at the United Nations, where she collects and uses data to tackle global challenges.
Read More about An International Student’s Path from RIT to the United Nations -
Turning Applied Stats into a Career in Insurance Underwriting
Andy Totman ’03 turned his Applied Statistics skills into a rewarding career in insurance underwriting, merging quantitative analysis with strategic decision-making.
Read More about Turning Applied Stats into a Career in Insurance Underwriting -
The Power of Being Data Literate in a Data-Driven World
The applied nature of the statistics programs at RIT helped Melissa Royo ’09/’10 get a sense for how real-world data behaves.
Read More about The Power of Being Data Literate in a Data-Driven World
Curriculum for 2024-2025 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 | ||
| ISCH-110 | 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 step-by-step 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 |
| MATH-181 | Calculus I (General Education – Mathematical Perspective A) This is the first in a two-course 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: MATH-111 or (NMTH-220 and NMTH-260 or NMTH-272 or NMTH-275) or equivalent courses with a minimum grade of B-, or a score of at least 60% on the RIT Mathematics Placement Exam.) Lecture 4 (Fall, Spring). |
4 |
| MATH-182 | Calculus II (General Education – Mathematical Perspective B) This is the second in a two-course 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 MATH-181 or MATH-181A or equivalent course.) Lecture 4 (Fall, Spring). |
4 |
| MATH-199 | Mathematics and Statistics Seminar This course provides an introduction to math and statistics software. The course provides practice in technical writing. Seminar 1 (Fall). |
1 |
| YOPS-10 | 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 first-year experiences, receive feedback, and develop a personal plan for future action in order to develop foundational self-awareness and recognize broad-based 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 | |
| Second Year | ||
| MATH-200 | 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: MATH-182 or equivalent course.) Lecture 3, Recitation 4 (Fall, Spring). |
3 |
| MATH-251 | 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 real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH-173 or MATH-182 or MATH 182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). |
3 |
| STAT-257 | 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: MATH-251.
NOTE: Students cannot receive credit for both MATH-252 and STAT-257 nor for both STAT-205 and STAT-257.) Lecture 3 (Fall, Spring). |
3 |
| MATH-399 | Mathematical Sciences Job Search Seminar This course helps students prepare to search for co-op or full-time 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 |
|
| MATH-221 | 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, vector-valued 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 MATH-219. (Prerequisite: C- or better MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). |
|
| MATH-221H | Honors Multivariable and Vector Calculus (General Education) This course is an honors version of MATH-221. 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 MATH-221, students in this course will often be expected to learn elementary skills and concepts from their text so that in-class 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 MATH-221. Students earning credit for this course cannot earn credit for MATH-219 or MATH-221. (Prerequisites: C or better in MATH-182 or MATH-173 or MATH-182A and Honors program status or at least a 3.2 cumulative GPA.) Lecture 4 (Fall). |
|
| Choose one of the following: | 3 |
|
| MATH-241 | 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: MATH-190 or MATH-200 or MATH-219 or MATH-220 or MATH-221 or MATH-221H or equivalent course.) Lecture 3 (Fall, Spring). |
|
| MATH-241H | 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: MATH-219 or MATH-221 or MATH-221H 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 | ||
| STAT-305 | Regression Analysis This course covers regression techniques with applications to the type of problems encountered in real-world 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: MATH-241 and (MATH-252 or STAT-205 or STAT-257) or equivalent courses.) Lecture 3 (Fall). |
3 |
| STAT-325 | Design of Experiments (WI-PR) This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include single-factor 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: STAT-205 or MATH-252 or STAT-257 or equivalent course.) Lecture 3 (Spring). |
3 |
Program Electives‡ |
15 | |
General Education – Immersion 1, 2 |
6 | |
General Education – Elective |
3 | |
| Fourth Year | ||
| STAT-405 | 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: STAT-205 or MATH-252 or STAT-257 or equivalent course.) Lecture 3 (Fall). |
3 |
| STAT-406 | Mathematical Statistics II This course is a continuation of STAT-405 covering classical and Bayesian methods in estimation theory, chi-square test, Neyman-Pearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT-405 or equivalent course.) Lecture 3 (Spring). |
3 |
| STAT-500 | Senior Capstone in Statistics (WI-PR) 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 open-ended 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: STAT-305 and STAT-325 or equivalent courses.) Lecture 3 (Spring). |
3 |
| STAT-501 | Experiential Learning Requirement in Statistics The experiential learning (EL) requirement may be fulfilled through a variety of methods including capstone, co-op, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. 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 (PHYS-211) and University Physics II (PHYS-212) or General & Analytical Chemistry I and Lab (CHMG-141/145) and General & Analytical Chemistry II and Lab (CHMG-142/146) or General Biology I and Lab (BIOL-101/103) and General Biology II and Lab (BIOL-102/104).
‡ Three of the six program electives must be from the following list of courses: Actuarial Mathematics (MATH-255), Topics in the Mathematics of Finance (MATH-261), Stochastic Processes (MATH-505), Introduction to Time Series (STAT-335), Nonparametric Statistics (STAT-345), Statistical Software - R (STAT-511), Statistical Quality Control (STAT-521), Data Mining (STAT-547), Survey Design and Analysis (STAT-572), Categorical Data Analysis (STAT-584). 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 (ACCT-110), Data Literacy, Analytics and Decision Making (BANA-255), Statistical Analysis for Bioinformatics (BIOL-470), Operations Management (DECS-310), Econometrics I (ECON-403), Financial Management (FINC-220), Financial Analytics (FINC-580), Lean Six Sigma Fundamentals ISEE-582), Marketing Analytics (MKTG-365)
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 | ||
| ISCH-110 | 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 step-by-step 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 |
| MATH-181 | Calculus I (General Education – Mathematical Perspective A) This is the first in a two-course 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: MATH-111 or (NMTH-220 and NMTH-260 or NMTH-272 or NMTH-275) or equivalent courses with a minimum grade of B-, or a score of at least 60% on the RIT Mathematics Placement Exam.) Lecture 4 (Fall, Spring). |
4 |
| MATH-182 | Calculus II (General Education – Mathematical Perspective B) This is the second in a two-course 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 MATH-181 or MATH-181A or equivalent course.) Lecture 4 (Fall, Spring). |
4 |
| MATH-199 | Mathematics and Statistics Seminar This course provides an introduction to math and statistics software. The course provides practice in technical writing. Seminar 1 (Fall). |
1 |
| YOPS-10 | 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 first-year experiences, receive feedback, and develop a personal plan for future action in order to develop foundational self-awareness and recognize broad-based 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 | ||
| MATH-200 | 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: MATH-182 or equivalent course.) Lecture 3, Recitation 4 (Fall, Spring). |
3 |
| MATH-251 | Probability and Statistics (General Education) 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 real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH-173 or MATH-182 or MATH 182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). |
3 |
| MATH-399 | Mathematical Science Job Search Seminar This course helps students prepare to search for co-op or full-time 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 |
|
| MATH-221 | 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, vector-valued 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 MATH-219. (Prerequisite: C- or better MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). |
|
| MATH-221H | Honors Multivariable and Vector Calculus (General Education) This course is an honors version of MATH-221. 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 MATH-221, students in this course will often be expected to learn elementary skills and concepts from their text so that in-class 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 MATH-221. Students earning credit for this course cannot earn credit for MATH-219 or MATH-221. (Prerequisites: C or better in MATH-182 or MATH-173 or MATH-182A and Honors program status or at least a 3.2 cumulative GPA.) Lecture 4 (Fall). |
|
| Choose one of the following: | 3 |
|
| MATH-241 | 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: MATH-190 or MATH-200 or MATH-219 or MATH-220 or MATH-221 or MATH-221H or equivalent course.) Lecture 3 (Fall, Spring). |
|
| MATH-241H | 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: MATH-219 or MATH-221 or MATH-221H or equivalent course and Honors program status or at least a 3.2 cumulative GPA.) Lecture 3 (Spring). |
|
| STAT-257 | 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: MATH-251.
NOTE: Students cannot receive credit for both MATH-252 and STAT-257 nor for both STAT-205 and STAT-257.) Lecture 3 (Fall, Spring). |
3 |
General Education – Immersion 1, 2 |
6 | |
General Education – Global Perspective |
3 | |
General Education –Social Perspective |
3 | |
General Education – Elective |
3 | |
| Third Year | ||
| STAT-305 | Regression Analysis This course covers regression techniques with applications to the type of problems encountered in real-world 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: MATH-241 and (MATH-252 or STAT-205 or STAT-257) or equivalent courses.) Lecture 3 (Fall). |
3 |
| STAT-325 | Design of Experiments (WI-PR) This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include single-factor 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: STAT-205 or MATH-252 or STAT-257 or equivalent course.) Lecture 3 (Spring). |
3 |
Open Electives |
9 | |
General Education – Immersion 3 |
3 | |
Program Electives‡ |
12 | |
| Fourth Year | ||
| MATH-606 | 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 ACMTH-MS or MATHML-PHD programs.) Lecture 2 (Fall). |
1 |
| MATH-607 | 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: MATH-606 or equivalent course or students in the ACMTH-MS or MATHML-PHD programs.) Lecture 2 (Spring). |
1 |
| STAT-405 | 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: STAT-205 or MATH-252 or STAT-257 or equivalent course.) Lecture 3 (Fall). |
3 |
| STAT-406 | Mathematical Statistics II This course is a continuation of STAT-405 covering classical and Bayesian methods in estimation theory, chi-square test, Neyman-Pearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT-405 or equivalent course.) Lecture 3 (Spring). |
3 |
| STAT-500 | Senior Capstone in Statistics (WI-PR) 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 open-ended 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: STAT-305 and STAT-325 or equivalent courses.) Lecture 3 (Spring). |
3 |
| STAT-501 | Experiential Learning Requirement in Statistics The experiential learning (EL) requirement may be fulfilled through a variety of methods including capstone, co-op, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. 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 | ||
| MATH-790 | Research & Thesis Masters-level 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 ACMTH-MS or MATHML-PHD 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 (PHYS-211) and University Physics II (PHYS-212) or General & Analytical Chemistry I and Lab (CHMG-141/145) and General & Analytical Chemistry II and Lab (CHMG-142/146) or General Biology I and Lab (BIOL-101/103) and General Biology II and Lab (BIOL-102/104).
‡ The four program electives must be from the following list of courses: Actuarial Mathematics (MATH-255), Topics in Mathematics of Finance (MATH-261), Stochastic Processes (MATH-505), Introduction to Time Series (STAT-335), Nonparametric Statistics (STAT-345), Statistical Software - R (STAT-511), Statistical Quality Control (STAT-521), Data Mining (STAT-547), Survey Design and Analysis (STAT-572), Categorical Data Analysis (STAT-584). 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 | ||
| ISCH-110 | 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 step-by-step 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 |
| MATH-181 | Calculus I (General Education – Mathematical Perspective A) This is the first in a two-course 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: MATH-111 or (NMTH-220 and NMTH-260 or NMTH-272 or NMTH-275) or equivalent courses with a minimum grade of B-, or a score of at least 60% on the RIT Mathematics Placement Exam.) Lecture 4 (Fall, Spring). |
4 |
| MATH-182 | Calculus II (General Education – Mathematical Perspective B) This is the second in a two-course 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 MATH-181 or MATH-181A or equivalent course.) Lecture 4 (Fall, Spring). |
4 |
| MATH-199 | Mathematics and Statistics Seminar This course provides an introduction to math and statistics software. The course provides practice in technical writing. Seminar 1 (Fall). |
1 |
| YOPS-10 | 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 first-year experiences, receive feedback, and develop a personal plan for future action in order to develop foundational self-awareness and recognize broad-based 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 | ||
| MATH-200 | 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: MATH-182 or equivalent course.) Lecture 3, Recitation 4 (Fall, Spring). |
3 |
| Choose one of the following: | 4 |
|
| MATH-221 | 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, vector-valued 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 MATH-219. (Prerequisite: C- or better MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). |
|
| MATH-221H | Honors Multivariable and Vector Calculus (General Education) This course is an honors version of MATH-221. 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 MATH-221, students in this course will often be expected to learn elementary skills and concepts from their text so that in-class 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 MATH-221. Students earning credit for this course cannot earn credit for MATH-219 or MATH-221. (Prerequisites: C or better in MATH-182 or MATH-173 or MATH-182A and Honors program status or at least a 3.2 cumulative GPA.) Lecture 4 (Fall). |
|
| Choose one of the following: | 3 |
|
| MATH-241 | 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: MATH-190 or MATH-200 or MATH-219 or MATH-220 or MATH-221 or MATH-221H or equivalent course.) Lecture 3 (Fall, Spring). |
|
| MATH-241H | 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: MATH-219 or MATH-221 or MATH-221H or equivalent course and Honors program status or at least a 3.2 cumulative GPA.) Lecture 3 (Spring). |
|
| MATH-251 | Probability and Statistics (General Education) 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 real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH-173 or MATH-182 or MATH 182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). |
3 |
| MATH-399 | Mathematical Science Job Search Seminar This course helps students prepare to search for co-op or full-time 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 |
| STAT-257 | 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: MATH-251.
NOTE: Students cannot receive credit for both MATH-252 and STAT-257 nor for both STAT-205 and STAT-257.) Lecture 3 (Fall, Spring). |
3 |
General Education – Immersion 1, 2 |
6 | |
General Education – Global Perspective |
3 | |
General Education – Social Perspective |
3 | |
General Education – Elective |
3 | |
| Third Year | ||
| STAT-305 | Regression Analysis This course covers regression techniques with applications to the type of problems encountered in real-world 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: MATH-241 and (MATH-252 or STAT-205 or STAT-257) or equivalent courses.) Lecture 3 (Fall). |
3 |
| STAT-325 | Design of Experiments (WI-PR) This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include single-factor 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: STAT-205 or MATH-252 or STAT-257 or equivalent course.) Lecture 3 (Spring). |
3 |
Open Electives |
9 | |
General Education - Immersion 3 |
3 | |
Program Electives‡ |
12 | |
| Fourth Year | ||
| MATH-606 | 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 ACMTH-MS or MATHML-PHD programs.) Lecture 2 (Fall). |
1 |
| MATH-607 | 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: MATH-606 or equivalent course or students in the ACMTH-MS or MATHML-PHD programs.) Lecture 2 (Spring). |
1 |
| STAT-405 | 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: STAT-205 or MATH-252 or STAT-257 or equivalent course.) Lecture 3 (Fall). |
3 |
| STAT-406 | Mathematical Statistics II This course is a continuation of STAT-405 covering classical and Bayesian methods in estimation theory, chi-square test, Neyman-Pearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT-405 or equivalent course.) Lecture 3 (Spring). |
3 |
| STAT-500 | Senior Capstone in Statistics (WI-PR) 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 open-ended 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: STAT-305 and STAT-325 or equivalent courses.) Lecture 3 (Spring). |
3 |
| STAT-501 | Experiential Learning Requirement in Statistics The experiential learning (EL) requirement may be fulfilled through a variety of methods including capstone, co-op, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. 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 | ||
| MATH-790 | Research & Thesis Masters-level 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 ACMTH-MS or MATHML-PHD 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 (PHYS-211) and University Physics II (PHYS-212) or General & Analytical Chemistry I and Lab (CHMG-141/145) and General & Analytical Chemistry II and Lab (CHMG-142/146) or General Biology I and Lab (BIOL-101/103) and General Biology II and Lab (BIOL-102/104).
‡ The four program electives must be from the following list of courses: Actuarial Mathematics (MATH-255), Topics in Mathematics of Finance (MATH-261), Stochastic Processes (MATH-505), Introduction to Time Series (STAT-335), Nonparametric Statistics (STAT-345),Statistical Software - R (STAT-511), Statistical Quality Control (STAT-521), Data Mining (STAT-547), Survey Design and Analysis (STAT-572), Categorical Data Analysis (STAT-584). 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 | ||
| ISCH-110 | 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 step-by-step 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 |
| MATH-181 | Calculus I (General Education – Mathematical Perspective A) This is the first in a two-course 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: MATH-111 or (NMTH-220 and NMTH-260 or NMTH-272 or NMTH-275) or equivalent courses with a minimum grade of B-, or a score of at least 60% on the RIT Mathematics Placement Exam.) Lecture 4 (Fall, Spring). |
4 |
| MATH-182 | Calculus II (General Education – Mathematical Perspective B) This is the second in a two-course 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 MATH-181 or MATH-181A or equivalent course.) Lecture 4 (Fall, Spring). |
4 |
| MATH-199 | Mathematics and Statistics Seminar I This course provides an introduction to math and statistics software. The course provides practice in technical writing. Seminar 1 (Fall). |
1 |
| YOPS-10 | 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 first-year experiences, receive feedback, and develop a personal plan for future action in order to develop foundational self-awareness and recognize broad-based 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 | ||
| MATH-200 | 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: MATH-182 or equivalent course.) Lecture 3, Recitation 4 (Fall, Spring). |
3 |
| MATH-251 | 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 real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH-173 or MATH-182 or MATH 182A or equivalent course.) Lecture 3, Recitation 1 (Fall, Spring, Summer). |
3 |
| MATH-399 | Mathematical Science Job Search Seminar This course helps students prepare to search for co-op or full-time 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 |
| STAT-257 | 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: MATH-251.
NOTE: Students cannot receive credit for both MATH-252 and STAT-257 nor for both STAT-205 and STAT-257.) Lecture 3 (Fall, Spring). |
3 |
| Choose one of the following: | 4 |
|
| MATH-221 | 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, vector-valued 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 MATH-219. (Prerequisite: C- or better MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 4 (Fall, Spring, Summer). |
|
| MATH-221H | Honors Multivariable and Vector Calculus (General Education) This course is an honors version of MATH-221. 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 MATH-221, students in this course will often be expected to learn elementary skills and concepts from their text so that in-class 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 MATH-221. Students earning credit for this course cannot earn credit for MATH-219 or MATH-221. (Prerequisites: C or better in MATH-182 or MATH-173 or MATH-182A and Honors program status or at least a 3.2 cumulative GPA.) Lecture 4 (Fall). |
|
| Choose one of the following: | 3 |
|
| MATH-241 | 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: MATH-190 or MATH-200 or MATH-219 or MATH-220 or MATH-221 or MATH-221H or equivalent course.) Lecture 3 (Fall, Spring). |
|
| MATH-241H | 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: MATH-219 or MATH-221 or MATH-221H 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 | ||
| STAT-641 | 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 APPSTAT-MS, SMPPI-ACT, or APPSTAT-U programs.) Lecture 3 (Fall, Spring, Summer). |
3 |
| STAT-642 | 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 APPSTAT-MS, SMPPI-ACT, or APPSTAT-U programs.) Lecture 3 (Spring, Summer). |
3 |
General Education – Immersion 2,3 |
6 | |
General Education – Electives |
6 | |
Program Electives‡ |
12 | |
| Fourth Year | ||
| STAT-405 | 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: STAT-205 or MATH-252 or STAT-257 or equivalent course.) Lecture 3 (Fall). |
3 |
| STAT-406 | Mathematical Statistics II This course is a continuation of STAT-405 covering classical and Bayesian methods in estimation theory, chi-square test, Neyman-Pearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT-405 or equivalent course.) Lecture 3 (Spring). |
3 |
| STAT-500 | Senior Capstone in Statistics (WI-PR) 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 open-ended 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: STAT-305 and STAT-325 or equivalent courses.) Lecture 3 (Spring). |
3 |
| STAT-501 | Experiential Learning Requirement in Statistics The experiential learning (EL) requirement may be fulfilled through a variety of methods including capstone, co-op, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. 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 | ||
| STAT-631 | 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 APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring). |
3 |
| STAT-790 | 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 (PHYS-211) and University Physics II (PHYS-212) or General & Analytical Chemistry I and Lab (CHMG-141/145) and General & Analytical Chemistry II and Lab (CHMG-142/146) or General Biology I and Lab (BIOL-101/103) and General Biology II and Lab (BIOL-102/104).
‡ Three of the six program electives must be from the following list of courses: Actuarial Mathematics (MATH-255), Topics in Mathematics of Finance (MATH-261), Stochastic Processes (MATH-505), Introduction to Time Series (STAT-335), Nonparametric Statistics (STAT-345), Statistical Software - R (STAT-511), Statistical Quality Control (STAT-521), Data Mining (STAT-547), Survey Design and Analysis (STAT-572), Categorical Data Analysis (STAT-584). 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.
First-Year Admission
First-year applicants are expected to demonstrate a strong academic background that 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 pre-calculus. Calculus is preferred.
- 2-3 years of science is required and must include chemistry or physics; both are recommended.
Transfer Admission
Transfer applicants should meet these minimum degree-specific requirements:
- A minimum of precalculus is required. Calculus is preferred
- Chemistry or physics is required.
Financial Aid and Scholarships
100% of all incoming first-year 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 real-world skills, enhanced problem-solving techniques, and broader career opportunities. Our students have opportunities to travel to national conferences for presentations and also become contributing authors on peer-reviewed manuscripts. Explore the variety of mathematics and statistics undergraduate research projects happening across the university.
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