Data Science MS

A degree driven by real-time employer demand

Data science jobs will grow 19% by 2030. Search our curriculum and you’ll find the expertise and skills most frequently posted by employers in this growing field.

The data science job market


Average Annual Salary for Data Scientists


Employment Growth for Data Scientists


Demand Growth for AI


Demand Growth for Machine Learning

Program Highlights

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 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. 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. 

This 30-credit degree may be completed in as few as 24 months. We offer Fall and Spring start dates.

Curriculum packed with high-demand skills

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Data Analytics

Data analysis skills are projected to grow in demand to 82% by 2026, and machine learning skills are growing by 1-2%.

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Data Science

Demand for skills in artificial intelligence is growing by 128% and deep learning skills demand is growing by 135%.

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Software and Programming

Expertise in Python and R is growing by 61%, while blockchain skills are growing by 245%.

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Experimental Design

Crossover, adaptive, and equivalence designs are dominating 38% of this job market.

What you will 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 R 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


Credits 3
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.
Credits 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.
Credits 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. 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.
Credits 3
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.
Credits 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.


Credits 1 - 3
This course will cover specialized topics in data science. Such topics are often emerging and not covered in other existing courses or are not covered in a manner that is appropriate for the student in this program. Graduate program standing and specific prerequisites will be noted for a specific special topic.
Credits 1 - 3
This course will cover advanced specialized topics data science. Such topics are may be emerging and advanced. Specific prerequisites will be noted for each specific special topic.
Credits 1 - 3
This course provides the graduate student an opportunity to explore an aspect of data science independently and in depth, under the direction of an advisor. The student selects a topic and then works with a faculty member to describe the value of the work and the deliverables.
Credits 3
This course covers the purpose, scope, capabilities, and processes used in data warehousing technologies for the management and analysis of data. Students will be introduced to the theory of data warehousing, dimensional data modeling, the extract/transform/load process, warehouse implementation, dimensional data analysis, and summary data management. The basics of data mining and importance of data security will also be discussed. Hands-on exercises include implementing a data warehouse.
Credits 3
This course provides a survey of the theory, concepts, and technologies related to representation and understanding of the earth - a scientific domain known as Geographic Information Science and Technology (GIS & T). Students will gain hands-on experience with technologies such as Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, Virtual Globes (Google Earth), and web mapping mashups. Furthermore, students will learn relevant GIS & T theory, concepts, and research trends such as spatial reasoning, spatiotemporal data representation, and spatial analysis.
Credits 3
Rapidly expanding collections of data from all areas of society are becoming available in digital form. Computer-based methods are available to facilitate discovering new information and knowledge that is embedded in these collections of data. This course provides students with an introduction to the use of these data analytic methods, with a focus on statistical learning models, within the context of the data-driven knowledge discovery process. Topics include motivations for data-driven discovery, sources of discoverable knowledge (e.g., data, text, the web, maps), data selection and retrieval, data transformation, computer-based methods for data-driven discovery, and interpretation of results. Emphasis is placed on the application of knowledge discovery methods to specific domains.
Credits 3
This course introduces students to Visual Analytics, or the science of analytical reasoning facilitated by interactive visual interfaces. Course lectures, reading assignments, and practical lab experiences will cover a mix of theoretical and technical Visual Analytics topics. Topics include analytical reasoning, human cognition and perception of visual information, visual representation and interaction technologies, data representation and transformation, production, presentation, and dissemination of analytic process results, and Visual Analytic case studies and applications. Furthermore, students will learn relevant Visual Analytics research trends such as Space, Time, and Multivariate Analytics and Extreme Scale Visual Analytics.
Credits 3
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows.
Credits 3
This course introduces areas of biological sciences in which mathematics can be used to capture essential interactions within a system. Different modeling approaches to various biological and physiological phenomena are developed (e.g., population and cell growth, spread of disease, epidemiology, biological fluid dynamics, nutrient transport, biochemical reactions, tumor growth, genetics). The emphasis is on the use of mathematics to unify related concepts.
Credits 3
This course introduces students to analysis of models with categorical factors, with emphasis on interpretation. Topics include the role of statistics in scientific studies, fixed and random effects, mixed models, covariates, hierarchical models, and repeated measures.
Credits 3
This course is designed to provide the student with solid practical skills in implementing basic statistical and machine learning techniques for the purpose of predictive analytics. Throughout the course, many real world case studies are used to motivate and explain the strengths and appropriateness of each method of interest. In those case studies, students will learn how to apply data cleaning, visualization, and other exploratory data analysis tools to a variety of real world complex data. Students will gain experience with reproducibility and documentation of computational projects and with developing basic data products for predictive analytics. The following techniques will be implemented and then tested with cross-validation: regularization in linear models, regression and smoothing splines, k-nearest neighbor, and tree-based methods, including random forest.
Credits 3
Multivariate data are characterized by multiple responses. This course concentrates on the mathematical and statistical theory that underlies the analysis of multivariate data. Some important applied methods are covered. Topics include matrix algebra, the multivariate normal model, multivariate t-tests, repeated measures, MANOVA principal components, factor analysis, clustering, and discriminant analysis.
Credits 3
This course is designed to provide the student with a solid practical hands-on introduction to the fundamentals of time series analysis and forecasting. Topics include stationarity, filtering, differencing, time series decomposition, time series regression, exponential smoothing, and Box-Jenkins techniques. Within each of these we will discuss seasonal and nonseasonal models.
Credits 3
An overview course in software engineering emphasizing software design and software development projects. The course will focus on object-oriented (OO) analysis, design principles and techniques. Students will be introduced to OO modeling, design patterns and design/code refactoring techniques. While there is a significant emphasis on product development, students will be required to use a rigorous process in a team-based product development project. Major topics include analysis and specification of software, subsystem modeling using patterns, and software testing. A term-long, team-based project is used to reinforce concepts presented in class. Programming is required.
Credits 3
Modeling plays a pivotal role during the software lifecycle during the pre-construction and post-construction activities of the software lifecycle. During the pre-construction stage, models help software engineers understand, specify, and analyze software requirements and designs. During the post-construction stage, models can be used to analyze software systems while in operation. This kind of analysis includes reliability and safety issues as well as timing constraint analysis. (Department approval)

Admission Requirements

  • Hold a baccalaureate (or equivalent) degree from a regionally accredited institution.
  • Have a minimum cumulative undergraduate GPA of 3.0 (B average), or related professional experience.
  • Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
  • Have prior study or professional experience in computer programming or complete bridge courses as required.
  • 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.
  • Submit a personal statement of educational objectives.
  • A test of English Language aptitude is required of all applicants and course registrants whose native language is not English.


The online MS in Data Science requires 30 credits and costs $1,191 per credit hour* (Academic Year 2020 – 2021). This tuition reflects the RIT Online discount of 43% off the MS in Data Science campus-based program cost. 

*Tuition costs may vary if edX Data Science MicroMasters certificate program is applied toward this degree. 

Keep in mind that there are many options available that may help you lower your costs including: 

  • Military tuition benefits
  • Support from employers 
  • Private scholarships 
  • Financing 
  • Payment plans

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