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Students
Tuition Fee
Start Date
Medium of studying
Duration
Program Facts
Program Details
Degree
Masters
Major
Artificial Intelligence | Data Analytics | Data Science
Area of study
Information and Communication Technologies
Course Language
English
About Program

Program Overview


Data Science Master of Science Degree

Overview

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.


RIT’s Data Science Master’s: On-Campus or Online

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.


What You'll Learn

  • Concepts and skills in machine learning to prepare you to build, tune, and discover actionable insights from predictive models
  • Programming language skills in Python and Java to be able to synthesize large unstructured data sets
  • Competencies in data mining, regression analysis, text mining, and predictive analytics
  • How to create, critically assess, interpret, and communicate rich visualizations

A Data Science Master’s Curriculum Packed with High-Demand Skills

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.


  • Data Analytics: Data analysis skills are projected to grow in demand to 82% by 2026, and machine learning skills are growing by 1-2%.
  • Data Science: Demand for skills in artificial intelligence is growing by 128% and deep learning skills demand is growing by 135%.
  • Software and Programming: Demand for expertise in Python is growing by 21%; blockchain skills are growing by 245%.
  • Experimental Design: Crossover, adaptive, and equivalence designs are dominating 38% of this job market.

Careers and Cooperative Education

Typical Job Titles

  • Data Scientist
  • Data Engineer
  • Data Architect
  • Machine Learning Engineer
  • Data Analyst
  • Database Administrator
  • Statistician
  • Business Analyst

Curriculum

Data Science, MS degree, typical course sequence (on-campus program)

  • First Year
    • DSCI-601 | Applied Data Science I | 3
    • DSCI-633 | Foundations of Data Science and Analytics | 3
    • DSCI-644 | Software Engineering for Data Science | 3
    • ISTE-608 | Database Design And Implementation | 3
    • STAT-614 | Applied Statistics | 3
    • SWEN-601 | Software Construction | 3
    • Electives | 3
  • Second Year
    • DSCI-602 | Applied Data Science II | 3
    • Electives | 6
  • Total Semester Credit Hours | 30

Data Science, MS degree, typical course sequence (online program)

  • First Year
    • DSCI-633 | Foundations of Data Science and Analytics | 3
    • ISTE-608 | Database And Implementation | 3
    • STAT-614 | Applied Statistics | 3
    • SWEN-601 | Software Construction | 3
    • Elective | 3
  • Second Year
    • DSCI-644 | Software Engineering for Data Science | 3
    • DSCI-799 | Graduate Capstone | 3
    • Electives | 9
  • Total Semester Credit Hours | 30

Data Science, MS degree, typical course sequence (online + edX program)

  • First Year
    • ISTE-608 | Database Design And Implementation | 3
    • SWEN-601 | Software Construction | 3
    • edX Micromasters | 9
    • Elective | 3
  • Second Year
    • DSCI-644 | Software Engineering for Data Science | 3
    • DSCI-799 | Graduate Capstone | 3
    • Electives | 6
  • Total Semester Credit Hours | 30

Admissions and Financial Aid

Application Details

To be considered for admission to the Data Science MS program, candidates must fulfill the following requirements:


  • Complete an online graduate application.
  • 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. A minimum cumulative GPA of 3.0 (or equivalent) is recommended.
  • Satisfy prerequisite requirements and/or complete bridge courses prior to starting program coursework.
  • Submit a current resume or curriculum vitae.
  • Submit a personal statement of educational objectives.
  • Submit two letters of recommendation.
  • Entrance exam requirements: GRE optional for Fall 2025 applicants. No minimum score requirement.

English Language Test Scores

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.


  • TOEFL | 88
  • IELTS | 6.5
  • PTE Academic | 60

Cost and Financial Aid

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.


Faculty

  • Christian Newman | Associate Professor
  • Michael Mior | Assistant Professor
  • Zhe Yu | Assistant Professor

Resources

Current students in the on-campus data science master’s program may refer to these resources for additional information.


Related News

  • January 15, 2025 | College of Computing to split Commencement into two ceremonies
  • April 29, 2024 | Students discover research opportunities on the path to graduation
  • April 3, 2023 | RIT Master Plan gives graduate tuition scholarship to eligible alumni

Contact

  • Bethany Iraci-McBane | Admissions Counselor
  • Travis Desell | On-Campus Program
  • Erik Golen | Online Program

Program Outline

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.

RIT’s Data Science Master’s: On-Campus or Online

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.

Read More

Students are also interested in: Information Technology and Analytics MS, Business Analytics MS, Applied Statistics MS, Health Informatics MS, Bioinformatics MS

This program is offered on-campus or online.

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

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.


SHOW MORE
About University
PhD
Masters
Bachelors
Diploma
Courses

Rochester Institute of Technology (Dubai)

Overview:

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.

Services Offered:

RIT Dubai provides a wide array of services to support student success, including:

Academic Support Center:


  • Offers tutoring, study skills workshops, and other resources to enhance academic performance.

Advising Resources:


  • Provides guidance on academic planning, career exploration, and personal development.

Health and Wellness:


  • Offers access to healthcare services, counseling, and wellness programs.

Athletics and Recreation:


  • Provides opportunities for students to participate in sports, fitness activities, and recreational programs.

Student Leadership:


  • Encourages student involvement in clubs, organizations, and leadership initiatives.

Student Accommodation:


  • Offers on-campus housing options for students.

Parking and Transportation:

  • Provides parking facilities and transportation services for students.

Student Life and Campus Experience:

RIT Dubai fosters a vibrant and inclusive campus community where students can engage in a variety of activities and experiences, including:

Student Life at RIT Dubai:


  • Offers opportunities for students to connect with peers, participate in social events, and explore cultural activities.

New Student Orientation:


  • Provides a welcoming introduction to campus life and resources.

Co-op and Internship Program:

  • Offers students practical work experience through co-op and internship opportunities.

Key Reasons to Study There:

American Degree:


  • RIT Dubai offers a true American degree, recognized globally for its quality and rigor.

State-of-the-Art Campus:


  • The campus features modern facilities and technology to support learning and research.

Co-op and Internship Program:


  • Provides students with valuable work experience and career development opportunities.

Study Abroad Options:


  • Offers students the chance to study at other RIT campuses or partner institutions around the world.

Global Connectivity:

  • RIT Dubai is located in a dynamic and international hub, providing students with diverse perspectives and networking opportunities.

Academic Programs:

RIT Dubai offers a range of undergraduate and graduate programs, including:

Undergraduate Programs:

  • Bachelor of Fine Arts in New Media Design
  • Bachelor of Science in Psychology
  • Bachelor of Science in Industrial Engineering
  • Bachelor of Science in Cybersecurity
  • Bachelor of Science in Computing and Information Technologies
  • Bachelor of Science in Electrical Engineering
  • Bachelor of Science in Mechanical Engineering
  • Bachelor of Science in Marketing
  • Bachelor of Science in Finance
  • Bachelor of Science in Global Business Management

Graduate Programs:

  • Master of Science in Organizational Leadership and Innovation
  • Masters of Science in Professional Studies: Future Foresight and Planning
  • Masters of Science in Engineering Management
  • Masters of Science in Mechanical Engineering
  • Masters of Science in Professional Studies: Data Analytics
  • Masters of Science in Professional Studies: Smart Cities
  • Masters of Science in Cybersecurity
  • Masters of Science in Electrical Engineering

Other:

  • RIT Dubai has a strong focus on innovation and entrepreneurship, with dedicated labs and centers supporting student projects and research.
  • The institution boasts a diverse student body representing over 75 nationalities, creating a rich and multicultural learning environment.
  • RIT Dubai has a high employability rate, with over 80% of graduates securing employment within six months of graduation.
  • The institution has a strong network of alumni, providing students with valuable connections and career support.

Total programs
226
Average ranking globally
#442
Average ranking in the country
#132
Admission Requirements

Data Science, MS degree, typical course sequence (on-campus program)

Course Sem. Cr. Hrs.
First Year
DSCI-601 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 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 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 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 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 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 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

 

Data Science, MS degree, typical course sequence (online program)

Course Sem. Cr. Hrs.
First Year
DSCI-633 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 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 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 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 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 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

 

Data Science, MS degree, typical course sequence (online + edX program)

Course Sem. Cr. Hrs.
First Year
ISTE-608 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 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 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 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

Note for online students

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.

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