Typical Job Titles
Data Scientist | Data Engineer |
Data Architect | Machine Learning Engineer |
Data Analyst | Database Administrator |
Statistician | Business Analyst |
Data science is one of the hottest fields in computing. The data science degree 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. RIT’s MS in data science encourages you to work with faculty experts in the fields of data science, analytics, and infrastructure who provide hands-on experience solving real problems. The curriculum includes opportunities for you 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 you—regardless of your scientific, engineering, or business background—to pursue a career in data science.
The explosive growth in demand for data science skills is disrupting today’s job markets. These skills are found in over half of all job postings related to this field.
To be considered for admission to the Data Science MS program, candidates must fulfill the following requirements:
International applicants whose native language is not English must submit one of the following official English language test scores. Some international applicants may be considered for an English test requirement waiver.
An RIT graduate degree is an investment with lifelong returns. Graduate tuition varies by degree, the number of credits taken per semester, and delivery method.
Current students in the on-campus data science master’s program may refer to these resources for additional information.
Data science is one of the hottest fields in computing. The data science degree 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. RIT’s MS in data science encourages you to work with faculty experts in the fields of data science, analytics, and infrastructure who provide hands-on experience solving real problems. The curriculum includes opportunities for you 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 you—regardless of your scientific, engineering, or business background—to pursue a career in data science.
RIT’s colleges of Science and Computing and Information Sciences collaborated to deliver the data science master's, 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.
The explosive growth in demand for data science skills is disrupting today’s job markets. These skills are found in over half of all job postings related to this field.
Designed for working professionals studying on campus or online part-time, this degree program has a strong career focus. You’ll learn both practical and theoretical skills to handle large-scale data management and analysis challenges ever-present in today’s data-driven organizations. This program places a unique focus on training data scientists in strong software engineering skills so that they can effectively develop real-world data science applications which operate within modern organizations' computational workflows. You’ll be learning with students from varied professional backgrounds and working with practitioners active in the field to provide hands-on experience solving real problems.
Students are also interested in: Information Technology and Analytics MS, Business Analytics MS, Applied Statistics MS, Health Informatics MS, Bioinformatics MS
Data Scientist | Data Engineer |
Data Architect | Machine Learning Engineer |
Data Analyst | Database Administrator |
Statistician | Business Analyst |
What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires.
Cooperative education is optional but strongly encouraged for graduate students in the data science MS degree.
Rochester Institute of Technology (Dubai) is a branch campus of the renowned Rochester Institute of Technology in the United States. Located in Dubai Silicon Oasis, a special economic zone for knowledge and innovation, RIT Dubai offers a comprehensive range of undergraduate and graduate programs in various fields, including engineering, business, computing, and design. The institution is committed to providing students with a high-quality American education in a dynamic and international setting.
RIT Dubai provides a wide array of services to support student success, including:
RIT Dubai fosters a vibrant and inclusive campus community where students can engage in a variety of activities and experiences, including:
RIT Dubai offers a range of undergraduate and graduate programs, including:
Course | Sem. Cr. Hrs. | |
---|---|---|
First Year | ||
DSCI-601 | Applied Data Science I |
3 |
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). | ||
DSCI-633 | Foundations of Data Science and Analytics |
3 |
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, semi-supervised 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. (This course is restricted to DATASCI-MS, INFOST-MS, SOFTENG-MS, COMPSCI-MS, or COMPIS-PHD Major students.) Lecture 3 (Fall, Spring). | ||
DSCI-644 | Software Engineering for Data Science |
3 |
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). | ||
ISTE-608 | Database Design And Implementation |
3 |
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). | ||
STAT-614 | Applied Statistics |
3 |
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). | ||
SWEN-601 | Software Construction |
3 |
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. (Corequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall). | ||
Electives |
3 | |
Second Year | ||
DSCI-602 | Applied Data Science II |
3 |
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. (Prerequisite: DSCI-601 or equivalent course.) Lecture 3 (Spring). | ||
Electives |
6 | |
Total Semester Credit Hours | 30 |
Course | Sem. Cr. Hrs. | |
---|---|---|
First Year | ||
DSCI-633 | Foundations of Data Science and Analytics |
3 |
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, semi-supervised 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. (This course is restricted to DATASCI-MS, INFOST-MS, SOFTENG-MS, COMPSCI-MS, or COMPIS-PHD Major students.) Lecture 3 (Fall, Spring). | ||
ISTE-608 | Database And Implementation |
3 |
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). | ||
STAT-614 | Applied Statistics |
3 |
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). | ||
SWEN-601 | Software Construction |
3 |
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. (Corequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall). | ||
Elective |
3 | |
Second Year | ||
DSCI-644 | Software Engineering for Data Science |
3 |
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). | ||
DSCI-799 | Graduate Capstone |
3 |
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). | ||
Electives |
9 | |
Total Semester Credit Hours | 30 |
Course | Sem. Cr. Hrs. | |
---|---|---|
First Year | ||
ISTE-608 | Database Design And Implementation |
3 |
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). | ||
SWEN-601 | Software Construction |
3 |
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. (Corequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall). | ||
edX Micromasters |
9 | |
Elective |
3 | |
Second Year | ||
DSCI-644 | Software Engineering for Data Science |
3 |
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). | ||
DSCI-799 | Graduate Capstone |
3 |
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). | ||
Electives |
6 | |
Total Semester Credit Hours | 30 |
The frequency of required and elective course offerings in the online program will vary, semester by semester, and will not always match the information presented here. Online students are advised to seek guidance from the listed program contact when developing their individual program course schedule.