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Mathematics×

Rochester Institute of Technology USA Masters Degrees in Mathematics

We have 5 Rochester Institute of Technology USA Masters Degrees in Mathematics

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The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as government. Sophisticated mathematical tools are increasingly used to develop new models, modify existing ones, and analyze system performance. Read more

Program overview

The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as government. Sophisticated mathematical tools are increasingly used to develop new models, modify existing ones, and analyze system performance. This includes applications of mathematics to problems in management science, biology, portfolio planning, facilities planning, control of dynamic systems, and design of composite materials. The goal is to find computable solutions to real-world problems arising from these types of situations.

The master of science degree in applied and computational mathematics provides students with the capability to apply mathematical models and methods to study various problems that arise in industry and business, with an emphasis on developing computable solutions that can be implemented. The program offers options in discrete mathematics, dynamical systems, and scientific computing. Students complete a thesis, which includes the presentation of original ideas and solutions to a specific mathematical problem. The proposal for the thesis work and the results must be presented and defended before the advisory committee.

Curriculum

Several options available for course sequence:
-Discrete mathematics option
-Dynamical systems option
-Scientific computing option

See website for individual module details.

Other entry requirements

-Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
-Submit a personal statement of educational objectives.
-Have an undergraduate cumulative GPA of 3.0 or higher.
-Submit two letters of recommendation, and complete a graduate application.
-International applicants whose primary language is not English must submit scores from the Test of English as a Foreign Language (TOEFL). A minimum score of 550 (paper-based) or 79-80 (Internet-based) is required. International English Language Testing System (IELTS) scores are accepted in place of the TOEFL exam. Minimum scores vary; however, the absolute minimum score required for unconditional acceptance is 6.5. For additional information about the IELTS, please visit http://www.ielts.org. Those who cannot take the TOEFL will be required to take the Michigan Test of English Proficiency at RIT and obtain a score of 80 or higher.
-Although Graduate Record Examination (GRE) scores are not required, submitting them may enhance a candidate's acceptance into the program.
-A student may also be granted conditional admission and be required to complete bridge courses selected from among RIT’s existing undergraduate courses, as prescribed by the student’s adviser. Until these requirements are met, the candidate is considered a nonmatriculated student. The graduate program director evaluates the student’s qualifications to determine eligibility for conditional and provisional admission.

Additional information

Student’s advisory committee:
Upon admission to the program, the student chooses an adviser and forms an advisory committee. This committee oversees the academic aspects of the student’s program, including the selection of a concentration and appropriate courses to fulfill the program’s requirements.

Cooperative education:
Cooperative education enables students to alternate periods of study on campus with periods of full-time, paid professional employment. Students may pursue a co-op position after their first semester. Co-op is optional for this program.

Part-time study:
The program is ideal for practicing professionals who are interested in applying mathematical methods in their work and enhancing their career options. Most courses are scheduled in the late afternoon or early evening. The program may normally be completed in two years of part-time study.

Nonmatriculated students:
A student with a bachelor’s degree from an approved undergraduate institution, and with the background necessary for specific courses, may take graduate courses as a nonmatriculated student with the permission of the graduate program director and the course instructor. Courses taken for credit may be applied toward the master’s degree if the student is formally admitted to the program at a later date. However, the number of credit hours that may be transferred into the program from courses taken at RIT is limited for nonmatriculated students.

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The master of science in computational finance is designed for students interested in computational or quantitative finance careers in banking, finance, and a growing number of additional industries. Read more

Program overview

The master of science in computational finance is designed for students interested in computational or quantitative finance careers in banking, finance, and a growing number of additional industries. Professionals in these fields use their strengths in business, modeling, and data analysis to understand and use complex financial models, often involving differential and stochastic calculus.

The program addresses a vital and growing career field, reaching beyond banking and finance. Typical job titles include risk analyst, research associate, quantitative analyst, quantitative structured credit analyst, credit risk analyst, quantitative investment analyst, quantitative strategist, data analyst, senior data analyst, fixed income quantitative analyst, and financial engineer. Computational finance is an excellent career option for technically-oriented professionals in the fields of business, math, engineering, economics, statistics, and computer science. Programming knowledge is highly preferred.

Plan of study

The curriculum offers an integration of finance, mathematics, and computing. The required mathematics courses have substantial financial content and the experiential computational finance course, which students take during the summer, makes use of skills learned in the mathematics, analytics, and finance courses taken up to that point. The program has a strong multidisciplinary nature and combines the expertise of four of RIT's colleges. The program is a full-time, 17-month curriculum beginning exclusively in the fall. The program ends with a required non-credit comprehensive exam based on the courses completed by the student.

Curriculum

Computational finance, MS degree, typical course sequence:
-Accounting for Decision Makers
-Survey of Finance
-Equity Analysis
-Debt Analysis
-Advanced Derivatives
-Mathematics for Finance I
-Mathematics for Finance II
-Analytics Electives
-Electives
-Computational Finance Experience

Other admission requirements

-Submit official transcripts (in English) from all previously completed undergraduate and graduate course work.
-Submit the results of the Graduate Management Admission Test (GMAT) or Graduate Record Exam (GRE) (GMAT preferred).
-Submit a personal statement (Applicants should explain why their background, please indicate mathematical and programming knowledge, and interests make them suitable for the program).
-Submit a current resume, and complete a graduate application.
-International applicants whose native language is not English must submit scores from the Test of English as a Foreign Language. Minimum scores of 580 (paper-based) or 92 (Internet-based) are required. Scores from the International English Language Testing System (IELTS) will be accepted in place of the TOEFL exam. The minimum acceptable score is 7.0. The TOEFL or IELTS requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions. For additional information on the IELTS, visit http://www.ielts.org.
-Completed applications for admission should be on file in the Office of Graduate Enrollment Services at least four weeks prior to registration for the next academic semester for students from the United States, and up to 10 weeks prior for international students applying for student visas.
-Accepted students can defer enrollment for up to one year. After one year, a new application must be submitted and will be re-evaluated based on the most current admission standards.

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The MS program in applied statistics is available to both full- and part-time students with courses available both on-campus and online. Read more

Program overview

The MS program in applied statistics is available to both full- and part-time students with courses available both on-campus and online. Cooperative education is optional. The program is intended for students who do not wish to pursue a degree beyond the MS. However, a number of students have attained doctorate degrees at other universities.

Plan of study

The program requires 30 credit hours and includes four core courses, five electives, and a capstone or thesis.

Core courses

There are four required core courses. Students, in conjunction with their advisers’ recommendations, should take the core courses early in the program.

Curriculum

Applied statistics, MS degree, typical course sequence:
First Year
-Statistical Software
-Fundamentals of Statistical Software
-Regression Analysis
-Foundations of Experimental Design
-Electives
Second Year
-Electives
-Capstone

Concentration areas

-Predictive Analytics
-Data Mining/Machine Learning
-Industrial
-Biostatistics
-Theory

Electives, capstone or thesis

Elective courses are chosen by the student with the help of their adviser. These courses are usually department courses but may include (along with transfer credits) up to 6 credit hours from other departments that are consistent with students’ professional objectives. The capstone course is designed to ensure that students can integrate the knowledge from their courses to solve more complex problems. This course is taken near the end of a student’s course of study. Students, with adviser approval, may write a thesis as their capstone.

Other admission requirements

-Have a satisfactory background in mathematics (one year of university-level calculus) and statistics (preferably two courses in probability and statistics).
-Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
-Submit a current resume.
-Submit two letters of recommendation, and complete a graduate application.
-International students whose native language is not English must submit scores from the Test of English as a Foreign Language (TOEFL).
-Scores from the Graduate Record Exam (GRE) are not required, however submitting scores may support the admission of an applicant who is deficient in certain admission requirements.

Additional information

Grades:
Students must attain an overall program grade-point average of 3.0 (B) for graduation.

Maximum time limit:
University policy requires that graduate programs be completed within seven years of the student's initial registration for courses in the program. Bridge courses are excluded.

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The MS in data science has a strong career focus and aims to prepare students with the practical and theoretical skills to handle large-scale data management and analysis challenges that arise in today's data-driven organizations. Read more

The MS in data science has a strong career focus and aims to prepare students with the practical and theoretical skills to handle large-scale data management and analysis challenges that arise in today's data-driven organizations.

The program enables students to work with active researchers in the field of data science, analytics, and infrastructure who can provide hands-on experience with real data and real problems. The curriculum includes opportunities for students to choose elective courses to pursue a variety of career paths within the broad field of data science and its various application areas. The goal of the program is to prepare students, regardless of their scientific, engineering, or business background, to pursue a career in data science.

The program is broad-based and comprehensive, combining computing and statistics courses. Core courses include statistics, data management, analytics, and software engineering. Elective courses provide students an opportunity to explore different areas of data science while a capstone project or thesis round out the program.

International Applications

International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 88 (internet-based) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.



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The Masters of Science degree program in Business Analytics (MS-BA) is designed to meet the rising national and international demand of businesses for professionals who can collect, analyze, and interpret the avalanche of data created in the context of economic activity. Read more

The Masters of Science degree program in Business Analytics (MS-BA) is designed to meet the rising national and international demand of businesses for professionals who can collect, analyze, and interpret the avalanche of data created in the context of economic activity.

This unique program will:

  • Address rising demand for business analysts of various types (e.g., pricing analyst, market analyst, process analyst, UX analyst) whose job entails the analysis of business-related data (e.g., transaction data for products, sentiments expressed by consumers) for the purpose of business decision making.
  • Position graduates in a world that appears increasingly data-centric (that is, more data generated and exponentially so) and data-decentralized (that is, both data collection and analysis is conducted within business units and sub-units rather than at the enterprise level).
  • Train graduates in the robust use of industry standard tools such as R and Python to solve business problems.
  • Train graduates to participate robustly in the entire value chain of business analytics - from strategy formulation to data collection/visualization to analysis and decision making.


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