Data Science Master of Science Degree
Data Science
Master of Science Degree
Demand is high for professionals skilled in both analytics and computing. Enhance your skill set by learning to manage large-scale data sets in this highly applied data science master's program.
Overview
One of the hottest fields in computing, the data science masters gives you the practical and theoretical skills to handle large-scale data management and analysis challenges that arise in today's data-driven organizations. This program appeals to professionals looking to enhance their skill set, and includes opportunities for customized course work within the broad field of data science and its various application areas.
In response to the growing need to generate and analyze meaningful data across all industries, demand is on the rise for a new breed of professionals skilled in both analytics and computing. The MS in data science encourages students to work with faculty experts in the field of data science, analytics, and infrastructure who provide hands-on experience solving 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 program prepares students—regardless of their scientific, engineering, or business background—to pursue a career in data science.
RIT’s colleges of Science and Computing and Information Sciences came together to deliver the MS in data science, which combines the expertise and knowledge from faculty in both colleges to provide you with a unique understanding of math, computing, and technology. This approach enhances your learning outcomes and increases career marketability.
Students are also interested in: Information Technology and Analytics MS, Business Analytics MS, Applied Statistics MS, Health Informatics MS, Bioinformatics MS
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Discover how graduate study at RIT can help further your career objectives.
Join us virtually
Discover how graduate study at RIT can help further your career objectives.
Join us virtually
Discover how graduate study at RIT can help further your career objectives.
Careers and Cooperative Education
Typical Job Titles
| Data Scientist | Data Engineer |
| Data Architect | Machine Learning Engineer |
| Data Analyst | Database Administrator |
| Statistician | Business Analyst |
Salary and Career Information for Data Science MS
Cooperative Education
Cooperative education, or co-op for short, is full-time, paid work experience in your field of study. And it sets RIT graduates apart from their competitors. It’s exposure–early and often–to a variety of professional work environments, career paths, and industries. RIT co-op is designed for your success.
Cooperative education is optional but strongly encouraged for graduate students in the data science MS degree.
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Curriculum for Data Science MS
Data Science, MS degree, typical course sequence (on-campus program)
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| DSCI-601 | Applied Data Science I This is the first of a two course applied data science seminar series. Students will be introduced to the data science masters program along with potential projects which they will develop over the course of this series in con-junction with the applied data science directed studies. Students will select a project along with an advisor and sponsor, develop a written proposal for their work, and investigate and write a related work survey to refine this proposal with their findings. Students will begin preliminary design and implementation of their project. Work will be presented in class for peer review with an emphasis on developing data science communication skills. This course will keep students up to date with the broad range of data science applications. (Prerequisites: SWEN-601 and DSCI-633 and STAT-614 or equivalent courses.) Lecture 3 (Fall). |
3 |
| DSCI-633 | Foundations of Data Science and Analytics A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semisupervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course. Lecture 3 (Fall, Spring). |
3 |
| DSCI-644 | Software Engineering for Data Science This course focuses on the software engineering challenges of building scalable and highly available big data software systems. Software design and development methodologies and available technologies addressing the major software aspects of a big data system including software architectures, application design patterns, different types of data models and data management, and deployment architectures will be covered in this course. (Prerequisites: SWEN-601 and DSCI-633 or equivalent courses.) Lecture 3 (Spring). |
3 |
| ISTE-608 | Database Design And Implementation An introduction to the theory and practice of designing and implementing database systems. Current software environments are used to explore effective database design and implementation concepts and strategies. Topics include conceptual data modeling, methodologies, logical/physical database design, normalization, relational algebra, schema creation and data manipulation, and transaction design. Database design and implementation projects are required. Lec/Lab 4 (Fall, Spring). |
3 |
| STAT-614 | Applied Statistics Statistical tools for modern data analysis can be used across a range of industries to help you guide organizational, societal and scientific advances. This course is designed to provide an introduction to the tools and techniques to accomplish this. Topics covered will include continuous and discrete distributions, descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one-way ANOVA and Chi-square tests. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall). |
3 |
| SWEN-601 | Software Construction This is a programming based course to enhance individual, technical engineering knowledge and skills as preparation for masters level graduate work in computing. Students will be introduced to programming language syntax, object oriented concepts, data structures and foundational algorithms. An emphasis will be placed on obtaining practical programming skills, through regular programming assignments and practicum. (Prerequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall). |
3 |
Electives |
3 | |
| Second Year | ||
| DSCI-602 | Applied Data Science II This is the second of a three course applied data science seminar series. Students will design an implementation plan and preliminary documentation for their selected applied data science project, along with an in class presentation of this work. At the end of the semester students will present preliminary demos of their project and write a preliminary project report. Writing and presentations will be peer reviewed to further enhance data science communication skills. This course will keep students up to date with the broad range of data science applications. (Prerequisites: DSCI-601 or equivalent course.
Co-requisites: DSCI-681 or equivalent course.) Lecture 3 (Spring). |
3 |
Electives |
6 | |
| Total Semester Credit Hours | 30 |
|
Data Science, MS degree, typical course sequence (online program)
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| DSCI-633 | Foundations of Data Science and Analytics A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semisupervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course. Lecture 3 (Fall, Spring). |
3 |
| ISTE-608 | Database And Implementation An introduction to the theory and practice of designing and implementing database systems. Current software environments are used to explore effective database design and implementation concepts and strategies. Topics include conceptual data modeling, methodologies, logical/physical database design, normalization, relational algebra, schema creation and data manipulation, and transaction design. Database design and implementation projects are required. Lec/Lab 4 (Fall, Spring). |
3 |
| STAT-614 | Applied Statistics Statistical tools for modern data analysis can be used across a range of industries to help you guide organizational, societal and scientific advances. This course is designed to provide an introduction to the tools and techniques to accomplish this. Topics covered will include continuous and discrete distributions, descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one-way ANOVA and Chi-square tests. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall). |
3 |
| SWEN-601 | Database Design And Implementation This is a programming based course to enhance individual, technical engineering knowledge and skills as preparation for masters level graduate work in computing. Students will be introduced to programming language syntax, object oriented concepts, data structures and foundational algorithms. An emphasis will be placed on obtaining practical programming skills, through regular programming assignments and practicum. (Prerequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall). |
3 |
Elective |
3 | |
| Second Year | ||
| DSCI-644 | Software Engineering for Data Science This course focuses on the software engineering challenges of building scalable and highly available big data software systems. Software design and development methodologies and available technologies addressing the major software aspects of a big data system including software architectures, application design patterns, different types of data models and data management, and deployment architectures will be covered in this course. (Prerequisites: SWEN-601 and DSCI-633 or equivalent courses.) Lecture 3 (Spring). |
3 |
| DSCI-799 | Graduate Capstone This non-class-based experience provides the student with an individual opportunity to explore a project-based or a research-based project that advances knowledge in an area of data science. The student selects a problem, conducts background research, develops the system or devises a research approach, analyses the results, and builds a professional document and presentation that disseminates the project. The report must include a literature review. The final report structure is to be determined by the capstone advisor. Ind Study (Fall, Spring, Summer). |
3 |
Electives |
9 | |
| Total Semester Credit Hours | 30 |
|
Data Science, MS degree, typical course sequence (online + edX program)
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| ISTE-608 | Database Design And Implementation An introduction to the theory and practice of designing and implementing database systems. Current software environments are used to explore effective database design and implementation concepts and strategies. Topics include conceptual data modeling, methodologies, logical/physical database design, normalization, relational algebra, schema creation and data manipulation, and transaction design. Database design and implementation projects are required. Lec/Lab 4 (Fall, Spring). |
3 |
| SWEN-601 | Software Construction This is a programming based course to enhance individual, technical engineering knowledge and skills as preparation for masters level graduate work in computing. Students will be introduced to programming language syntax, object oriented concepts, data structures and foundational algorithms. An emphasis will be placed on obtaining practical programming skills, through regular programming assignments and practicum. (Prerequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall). |
3 |
edX Micromasters |
9 | |
Elective |
3 | |
| Second Year | ||
| DSCI-644 | Software Engineering for Data Science This course focuses on the software engineering challenges of building scalable and highly available big data software systems. Software design and development methodologies and available technologies addressing the major software aspects of a big data system including software architectures, application design patterns, different types of data models and data management, and deployment architectures will be covered in this course. (Prerequisites: SWEN-601 and DSCI-633 or equivalent courses.) Lecture 3 (Spring). |
3 |
| DSCI-799 | Graduate Capstone This non-class-based experience provides the student with an individual opportunity to explore a project-based or a research-based project that advances knowledge in an area of data science. The student selects a problem, conducts background research, develops the system or devises a research approach, analyses the results, and builds a professional document and presentation that disseminates the project. The report must include a literature review. The final report structure is to be determined by the capstone advisor. Ind Study (Fall, Spring, Summer). |
3 |
Electives |
6 | |
| Total Semester Credit Hours | 30 |
|
Admission Requirements
To be considered for admission to the MS in data science, candidates must fulfill the following requirements:
- Complete an online graduate application. Refer to Graduate Admission Deadlines and Requirements for information on application deadlines, entry terms, and more.
- Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned.
- Hold a baccalaureate degree (or US equivalent) from an accredited university or college.
- Recommended minimum cumulative GPA of 3.0 (or equivalent).
- Submit a current resume or curriculum vitae.
- Two letters of recommendation are required. Refer to Application Instructions and Requirements for additional information.
- Not all programs require the submission of scores from entrance exams (GMAT or GRE). Please refer to the Graduate Admission Deadlines and Requirements page for more information.
- Submit a personal statement of educational objectives. Refer to Application Instructions and Requirements for additional information.
- Have college level credit or practical experience in computer programming and statistics.
- International applicants whose native language is not English must submit official test scores from the TOEFL, IELTS, or PTE. Students below the minimum requirement may be considered for conditional admission. Refer to Graduate Admission Deadlines and Requirements for additional information on English requirements. International applicants may be considered for an English test requirement waiver. Refer to Additional Requirements for International Applicants to review waiver eligibility.
Please note: Certain countries are subject to comprehensive embargoes under US Export Controls, which prohibit virtually ALL exports, imports, and other transactions without a license or other US Government authorization. Learners from Syria, Sudan, North Korea, the Crimea region of the Ukraine, Iran, and Cuba may not register for RIT online courses. Nor may individuals on the United States Treasury Department’s list of Specially Designated Nationals or the United States Commerce Department’s table of Deny Orders. By registering for RIT online courses, you represent and warrant that you are not located in, under the control of, or a national or resident of any such country or on any such list.
Learn about admissions, cost, and financial aid
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