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. 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. 

This 30-credit degree may be completed entirely through RIT Online, or students may be awarded up to 9 credits from an edX Data Science MicroMasters program toward RIT's MS Data Science degree. 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

Demand for expertise in Python is growing by 21%; 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 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


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
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.
Credits 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.
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. 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.
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 3 - 6
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.


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
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 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
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
This course introduces students to data management and analytics in a business setting. Students learn how to formulate hypotheses, collect and manage relevant data, and use standard tools such as Python and R in their analyses. The course exposes students to structured data as well as semi-structured and unstructured data. There are no pre or co-requisites; however, instructor permission is required for students not belonging to the MS-Business Analytics or other quantitative programs such as the MS-Computational Finance which have program-level pre-requisites in the areas of calculus, linear algebra, and programming.
Credits 3
This course provides foundational, advanced knowledge in the realm of business analytics. Advanced topics such as machine learning, analysis of structured data, text mining, and network analysis are covered. Industry standard tools such as R and Python are extensively used in completing student projects.
Credits 3
This course provides a broad introduction to database management systems including data modeling, the relational model, and SQL. Database system implementation issues are covered next, where the focus is on data structures and algorithms used to implement database management systems. Topics include physical data organizations, indexing and hashing, query processing and optimization, database recovery techniques, transaction management, concurrency control, and database performance evaluation. Current research topics in database system implementation are also explored. Programming projects, a term paper, and presentations will be required. Note: Students who take this course may not take CSCI-421 for credit.
Credits 3
This course examines policies, methods and mechanisms for securing enterprise and personal data and ensuring data privacy. Topics include data integrity and confidentiality; access control models; secure database architectures; secure transaction processing; information flow, aggregation, and inference controls; auditing; securing data in contemporary (relational, XML and other NO SQL) database systems; data privacy; and legal and ethical issues in data protection. Programming projects are required.
Credits 3
This course provides a graduate-level introduction to the concepts and techniques used in data mining. Topics include the knowledge discovery process; prototype development and building data mining models; current issues and application domains for data mining; and legal and ethical issues involved in collecting and mining data. Both algorithmic and application issues are emphasized to permit students to gain the knowledge needed to conduct research in data mining and apply data mining techniques in practical applications. Data mining projects, a term paper, and presentations are required.
Credits 3
This is a graduate level introduction to the field of computing security. An extensive overview of various branches of computing security areas will be presented including concepts, issues, and tools that are critical in solving problems in computing security domain. Students will have opportunities to learn essential techniques in protecting systems and network infrastructures, analyzing and monitoring potential threats and attacks, devising and implementing security solutions for organizations large or small.
Credits 3
Human-computer interaction (HCI) is a field of study concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them. This course surveys the scope of issues and foundations of the HCI field: cognitive psychology, human factors, interaction styles, user analysis, task analysis, interaction design methods and techniques, and evaluation. This course will focus on the users and their tasks.
Credits 3
Designing meaningful relationships among people and the products they use is both an art and a science. This course will focus on the unique design practice of: representing and organizing information in such a way as to facilitate perception and understanding (information architecture); and, specifying the appropriate mechanisms for accessing and manipulating task information (interaction design). This course will also explore the various design patterns (design solutions to particular problems) that are appropriate for the HCI professional. Students will need prior knowledge of an interface prototyping tool.
Credits 3
This course will cover a wide range of current topics in modern image processing and computer vision. Topics will include introductory concepts in supervised and unsupervised machine learning, linear and nonlinear filtering, image enhancement, supervised and unsupervised image segmentation, object classification, object detection, feature matching, image registration, and the geometry of cameras. Assignments will involve advanced computational implementations of selected topics from the current literature in a high-level language such as Python, MATLAB, or Julia and will be summarized by the students in written technical papers. The course requires computer programming, linear algebra, and calculus.
Credits 3
This course provides students with exposure to foundational information sciences and technologies. Topics include an overview of data types, structuring and processing data and knowledge, data transformation, and data storage and warehousing. Students will work with non-traditional (noSQL) data stores to manage large datasets in the context of specific problem scenarios.
Credits 3
This course provides a rigorous introduction to the principles of medical informatics. The focus of this course is on the study of the nature of medical information and its use in clinical practice and clinical quality improvement. Key topics include: the electronic medical record (EMR) and its impact on health care delivery, the Internet and mobile computing as sources of medical information, Health care information systems, the software development lifecycle, the importance of the informatics specialists in medicine and the various roles they can play, and government economic incentives and policy issues in healthcare such as privacy, confidentiality, including health care regulatory and accreditation issues and the Health Insurance Portability and Accountability Act (HIPAA). Students will participate in online discussion of medical informatics. They will also investigate several topics of interest in the field and provide presentations.
Credits 3
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.
Credits 3
This course provides an overview of marketing analytics in the context of marketing research, product portfolios, social media monitoring, sentiment analysis, customer retention, clustering techniques, and customer lifetime value calculation. Students will be introduced to, mathematical and statistical models used in these applications and their implementation using statistical tools and programming languages such as SAS, SPSS, Python and R. Multiple data sources will be used ranging from structured data from company databases, scanner data, social media data, text data in the form of customer reviews, and research databases. Students will complete guided projects using real time data and make effective use of visualization to add impact to their reports. There are no listed pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.
Credits 3
This course is an introduction to the statistical-software package R, which is often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions; graphics; matrices and arrays; simulations and app development with Shiny.
Credits 3
A practical course designed to provide in-depth understanding of the principles and practices of statistical process control, process capability, and acceptance sampling. Topics include: statistical concepts relating to processes, Shewhart charts for attribute and variables data, CUSUM charts, EWMA charts, process capability studies, attribute and variables acceptance sampling techniques.
Credits 3
This course introduces principles of probability and statistics with a strong emphasis on conceptual aspects of statistical inference. Topics include fundamentals of probability, probability distribution functions, expectation and variance, discrete and continuous distributions, sampling distributions, confidence intervals and hypothesis tests.
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
The emphasis of this course is how to make valid statistical inference in situations when the typical parametric assumptions no longer hold, with an emphasis on applications. This includes certain analyses based on rank and/or ordinal data and resampling (bootstrapping) techniques. The course provides a review of hypothesis testing and confidence-interval construction. Topics based on ranks or ordinal data include: sign and Wilcoxon signed-rank tests, Mann-Whitney and Friedman tests, runs tests, chi-square tests, rank correlation, rank order tests, Kolmogorov-Smirnov statistics. Topics based on bootstrapping include: estimating bias and variability, confidence interval methods and tests of hypothesis.

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 free online foundation courses in Java and Python 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.

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