Data Science Master of science degree
Data Science
Master of science degree
Overview
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.
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. By 2020, data science job openings are projected to grow by 15 percent. 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.
The data science master's is a collaborative program hosted by Golisano College of Computing and Information Sciences and College of Science. You'll take courses that provide deep learning taught by RIT faculty who are experts in the field of data science. You’ll learn the skills that are recognized by employers for their real job relevance. The program is available both online and on-campus.
Featured Profiles
Your Partners in Success: Meet Our Faculty
Dr. Tony Wong
Mathematics is a powerful tool for answering questions. From mitigating climate risks to splitting the dinner bill, Professor Wong shows students that math is more than just a prerequisite.
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 sci-ence 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
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. Major families of data analysis techniques covered include classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of programming assignments which will involve implementation of specific techniques on practical datasets from diverse application domains, reinforcing the concepts and techniques covered in lectures. 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: DSCI-623 or equivalent course.) Lecture 3 (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 |
6 | |
| 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-623 |
Introduction to Data Science: Management
This course introduces students to the problems and issues in managing large sets of data, focusing on modeling, storing, searching, and transforming large collections of data for analysis. The course will cover database management and information retrieval systems, including relational database systems, massively parallel/distributed computation models (e.g., MapReduce/Hadoop) and various NoSQL (e.g., key-value, document, column, and graph) systems that are designed to handle extremely large-scale and complex data collections. Emphasis is placed on the application of large-scale data management techniques to particular domains. Programming projects are required. (Graduate Computing and Information Sciences) Lecture 3 (Fall Or Spring).
|
3 |
| DSCI-633 |
Foundations of Data Science
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. Major families of data analysis techniques covered include classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of programming assignments which will involve implementation of specific techniques on practical datasets from diverse application domains, reinforcing the concepts and techniques covered in lectures. Lecture 3 (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 |
| 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 course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
|
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: DSCI-623 or equivalent course.) 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 | ||
| DSCI-623 |
Introduction to Data Science: Management
This course introduces students to the problems and issues in managing large sets of data, focusing on modeling, storing, searching, and transforming large collections of data for analysis. The course will cover database management and information retrieval systems, including relational database systems, massively parallel/distributed computation models (e.g., MapReduce/Hadoop) and various NoSQL (e.g., key-value, document, column, and graph) systems that are designed to handle extremely large-scale and complex data collections. Emphasis is placed on the application of large-scale data management techniques to particular domains. Programming projects are required. (Graduate Computing and Information Sciences) Lecture 3 (Fall Or Spring).
|
3 |
| 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 course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
|
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: DSCI-623 or equivalent course.) 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 a graduate application.
- Hold a baccalaureate degree (or equivalent) from an accredited university.
- Have prior knowledge or professional experience in computer programming and statistics.
- Submit transcripts (in English) of all previously completed undergraduate and graduate course work
- Submit a minimum of two recommendations from individuals who are well-qualified to assess the applicant’s potential for success.
- Submit a current resume or curriculum vitae.
- Applicants with undergraduate degrees from foreign colleges and universities are required to submit GRE scores.
- Submit a personal statement of educational objectives.
- Have a minimum cumulative GPA of 3.0 (or equivalent).
- 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.
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
Latest News
-
May 4, 2020
![two students working in room with orange-tinted windows.]()
RIT Class of 2020 Master Plan gives grads a solid next step
RIT has announced a tuition scholarship program for this year’s RIT graduates seeking to further advance their career opportunities while the job market recalibrates and the country responds to the coronavirus pandemic. The RIT Class of 2020 Master Plan includes a special scholarship covering 55% of graduate tuition. RIT students who graduate in May or August this year may be eligible for this scholarship.
-
March 15, 2019
![Group of five students and professor sit around table wearing T-shirts that read: DataFest]()
Data science community to gather at RIT for regional DataFest hackathon March 29-31
Teams of three to five students will have 48 hours to mine a complex data set pertaining to a real-world problem. Teams will test their statistical analytic and data science skills to find the best solution.
