Data Science MS
Data Science: Online
Master of science degree
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
117k+
Average Annual Salary for Data Scientists
19%
Employment Growth for Data Scientists
190%
Demand Growth for AI
35%
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
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.
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
Curriculum
STAT-614
Applied Statistics
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.
SWEN-601
Software Construction
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.
ISTE-608
Database Design And Implementation
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.
DSCI-633
Foundations of Data Science and Analytics
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, semisupervised 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.
DSCI-644
Software Engineering for Data Science
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.
DSCI-799
Graduate Capstone
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.
Electives
DSCI-689
Topics In Data Science
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.
DSCI-789
Advanced Topics In Data Science
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.
DSCI-790
Independent Study
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.
ISTE-724
Data Warehousing
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.
ISTE-740
Geographic Information Science And Technology
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.
ISTE-780
Data Driven Knowledge Discovery
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.
ISTE-782
Visual Analytics
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.
MATH-605
Stochastic Processes
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.
MATH-761
Mathematical Biology
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.
STAT-642
Applied Linear Models - ANOVA
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.
STAT-745
Predictive 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.
STAT-756
Multivariate Analysis
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.
STAT-773
Time Series Analysis and Forecasting
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.
SWEN-610
Foundations of Software Engineering
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.
SWEN-745
Software Modeling
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 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.
Cost
The online MS in Data Science requires 30 credits and costs $1,237 per credit hour* (Academic Year 2021 – 2022). 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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