Information Technology and Analytics Master of Science Degree

In this information technology master’s degree, you’ll engage in an applied IT program to understand how Big Data is collected and managed, and how its analysis informs both industry decision making and IT solutions in the computing industry.


Outcomes Rate of RIT Graduates from this degree


Median First-Year Salary of RIT Graduates from this degree


Database administrator job growth


Job listings with SQL skills


Average salary nationwide


Demand growth for data analysis in this field

Overview for Information Technology and Analytics MS

  • Combine IT and decision making through analytics to address the challenges and opportunities of Big Data.
  • Leverage the most current data analytics techniques to address and solve industry problems.
  • Complete the master's of information technology and analytics on-campus or online.

Technology has woven itself into the fabric of society, binding people and information closer together than ever before. This evolving digital era brings with it exciting innovations. It also brings a host of new, unexplored problems that can be unlocked through data analytics. RIT’s master’s of information technology and analytics provides an opportunity for in-depth, career-oriented study that explores how information is understood and leverages the most current data analytics techniques to address industry problems.

The internet has brought a new kind of democracy where all information is created equal. No longer the sole province of experts and the traditional media, it has become grassroots, viral, and global. The sheer volume and lightning speed of information transfer has changed how the world communicates, educates, learns, and ultimately solves problems. As the web and its related technologies evolve, users need help in managing these new tools.

A Growing, In-Demand Field

Demand for information technologists, data scientists, and data-driven decision-makers across all fields now comprise one-third of the data-savvy professional job market. The MS degree in information technology and analytics addresses this demand at the intersection of information technology and data science. With a program rich in analytics and in-depth, career-oriented study, you will explore how information is organized, verified, analyzed, and applied in today's data-rich environment.

An Information Technology Master’s Degree That Combines IT and Analytics

Graduate study in a computing discipline that only focuses on traditional computing approaches is not flexible enough to meet the needs of the real world. New hardware and software tools are continually introduced into the market. IT professionals must have a specific area of expertise, as well as adaptability, to tackle the next new thing. Or, just as often, retrofit available technologies to help users adapt to the latest trends.

RIT’s master’s of information technology and analytics provides an opportunity for in-depth study that prepares you for today’s high-demand computing careers. Companies are drowning in data—structured, semi-structured, and unstructured. Big data is not just high transaction volumes; it is also data in various formats, with high velocity change, and increasing complexity. Information is gleaned from unstructured sources—such as web traffic or social networks—as well as traditional ones; and information delivery must be immediate and on demand.

This degree shares curriculum with the data science master's degree, and is of particular value to professionals in the field of information technology who need to upskill in data science knowledge to handle the huge volumes of data organizations must utilize. In this degree, you will apply critical, analytical thinking to database design, management, and mining. 

As the users’ advocate, IT professionals also need the critical thinking skills to problem-solve in a wide variety of computing situations, combined with an understanding of the needs of their audience. Just knowing how technology works is no longer enough. Today, computing professionals need to know how to make it all work.

RIT’s Master’s of Information Technology and Analytics: On-Campus or Online

The master’s of information technology and analytics program addresses the web systems and integration technologies, and the information management and database technology pillars, of the IT academic discipline, along with the additional option of discovery informatics.

Domain Electives–Chosen only by those enrolled in the on-campus option, domain electives are available in: analytics, information management and database technology, or web systems and integration technologies. With permission of the graduate program director, students may select the special topics track to fulfill this requirement. See the graduate program director for more information.

Thesis/Capstone Options–For the on-campus option of the program, students may choose a project or a thesis to build upon their domain of study. The project option requires one additional domain elective. The thesis option does not require an additional elective. The online option consist of a capstone project.

An IT and Analytics Curriculum Packed with High-Demand Skills

In this degree, you will apply critical, analytical thinking to database design, management, and mining. 

  • Data Analytics: Demand for data analytics skills are growing 82%, and expertise in big data carries a salary premium.
  • Data Visualization: Demand for Tableau skills are growing 87%.
  • Software and Programming: Demand for these skills is growing 61%.
  • SQL: Nearly half of all postings for jobs in this field require advanced SQL skills.

Demand for information technologists, data scientists, and data-driven decision-makers across all fields now comprise one-third of the data-savvy professional job market. The MS degree in information technology and analytics addresses this demand at the intersection of information technology and data science. With a program rich in analytics and in-depth, career-oriented study, you will explore how information is organized, verified, analyzed, and applied in today's data-rich environment.

This program is offered on-campus or online.

Careers and Cooperative Education

Typical Job Titles

Data Analyst Data Engineer
Full-Stack Software Engineer Web/Mobile Developer
Database Administrator Data Warehouse Analyst
Technical Product Manager

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 MS in information technology and analytics.

Curriculum for 2023-2024 for Information Technology and Analytics MS

Current Students: See Curriculum Requirements

Information Technology and Analytics (thesis and project options), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
Foundations of Data Science and Analytics
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).
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).
Database Design and Implementation
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).
Non-Relational Data Management
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. (Prerequisites: ISTE-608 or DSCI-623 or CSCI-620 or equivalent course.) Lec/Lab 3 (Fall, Spring).
Information Retrieval and Text Mining
This course provides students with exposure to foundational data analytics technologies, focusing on unstructured data. Topics include unstructured data modeling, indexing, retrieval, text classification, text clustering, and information visualization. (Prerequisites: ISTE-608 and (DECS-782 or STAT-145 or STAT-614) or equivalent courses.) Lec/Lab 3 (Fa/sp/su).
Visual Analytics
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. Lec/Lab 3 (Spring).
Second Year
Choose one of the following:
 Thesis in Information Sciences and Technologies
The thesis capstone experience for the Master of Science in Information Technology and Analytics program. Students must submit an approved capstone proposal in order to enroll. (Permission of capstone committee and graduate coordinator). (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
 Project In Information Sciences And Technologies
The project-based culminating experience for the Master of Science in Information Technology and Analytics program. A MS project will typically include a software system development component requiring a substantial and sustained level of effort. Students must submit an approved project proposal in order to enroll. (Permission of project committee and graduate program director). (Enrollment in this course requires permission from the department offering the course.) Project 3 (Fall, Spring, Summer).
Scholarship in Information Technology and Analytics
ITA graduate students are expected to make a scholarly contribution as a requirement for the MS degree. The Scholarship in Information Technology and Analytics course provides students with the fundamental skills needed to define and conduct a program of scholarly investigation in the form of a capstone or thesis project. The course focuses on skills such as academic writing, searching the literature, identifying and articulating interesting and important topics and problems, scholarship ethics, developing capstone proposals, critical thinking, and effective oral and written communication and presentation of scholarship. (This course is restricted to INFOST-MS, INFOTEC-MS and NETSYS-MS students.) Lecture 3 (Fall, Spring, Summer).
Total Semester Credit Hours

Electives List

Information Assurance Fundamentals
This course provides an introduction to the topic of information assurance as it pertains to an awareness of the risks inherent in protecting digital content in today’s networked computing environments. Topics in secure data and information access will be explored from the perspectives of software development, software implementation, data storage, and system administration and network communications. The application of computing technologies, procedures and policies and the activities necessary to detect, document, and counter unauthorized data and system access will be explored. Effective implementation will be discussed and include topics from other fields such as management science, security engineering and criminology. A broad understanding of this subject is important for computing students who are involved in the architecting and creation of information and will include current software exploitation issues and techniques for information assurance. Lec/Lab 3 (Spring).
Data Warehousing
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. (Prerequisites: ISTE-608 or equivalent course.) Lec/Lab 3 (Fall, Spring).
Foundations of IOT
Internet of Things (IoT) refers to physical and virtual objects that are connected to the Internet to provide intelligent services for energy management, logistics, retail, agriculture and many other domains. IoT leverages sensors, wireless communication, mobile devices, networking and cloud technologies to create many smart applications. In this course, the students learn about IoT design and development methodologies that enable the development of IoT applications. The students have hands-on opportunities to program and build IoT prototypes through lab assignments and a course project. The students should have some programming knowledge and required to purchase a IoT kit. (This course is restricted to students in INFOST-MS.) Lecture 3 (Spring).
IOT Analytics
IoT is simply interconnected devices that generate and exchange data from observations, facts, and other data, making it available to anyone. This includes devices that generate data from sensors, smart phones, appliances, and home network devices. IoT solutions are designed to make our knowledge of the world around us more aware and relevant, making it possible to get data about anything from anywhere at any time. This course teaches how IoT data could help and execute data driven operational and business decisions. The students learn how IoT analytics can create adaptive business and operational decisions in intelligent, effective and efficient ways. First, this course provides students with an understanding of different types of IoT data and the knowledge of how to handle the data relate to IoT. Then, the students learn how to create and setup a cloud analytic environment, exploring IoT data. The course also teaches how to apply analytics and statistics to extract value from the data. Lastly, the course explores different use-cases for IoT data. Purchasing a IoT kit is required. (This course is restricted to INFOST-MS or HUMCOMP-MS or DATASCI-MS students.) Lec/Lab 3 (Fall).
Project Management
Information technology projects require the application of sound project management principles in order to be developed on time, on budget, and on specification. This course takes students through the nine knowledge areas of modern project management and the utilization of project management principles in both traditional and agile environments. Lecture 3 (Fall).
Data Driven Knowledge Discovery
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. (Prerequisite: DSCI-633 or equivalent course.) Lec/Lab 3 (Fall, Summer).

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.

Admissions and Financial Aid

This program is available on-campus or online.

On Campus

Offered Admit Term(s) Application Deadline STEM Designated
Full-time Fall or Spring Rolling Yes
Part-time Fall or Spring Rolling No


Offered Admit Term(s) Application Deadline STEM Designated
Part-time Fall Rolling No

Full-time study is 9+ semester credit hours. Part-time study is 1‑8 semester credit hours. International students requiring a visa to study at the RIT Rochester campus must study full‑time.

Application Details

To be considered for admission to the Information Technology and Analytics MS program, candidates must fulfill the following requirements:

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.

88 6.5 60

International students below the minimum requirement may be considered for conditional admission. Each program requires balanced sub-scores when determining an applicant’s need for additional English language courses.

How to Apply Start or Manage Your Application

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. View the general cost of attendance or estimate the cost of your graduate degree.

A combination of sources can help fund your graduate degree. Learn how to fund your degree

Additional Information


It is expected that prospective students will have a background in fundamental information technology concepts including object-oriented programming, website development, database theory and practice, and statistics. Students without the necessary background should complete the prerequisites before applying to the program. However, bridge courses are available to satisfy the prerequisites.

Bridge Courses

Students whose undergraduate preparation or employment experience does not satisfy the prerequisites can make up for these deficiencies by completing prerequisite bridge courses as prescribed by the graduate program director. The bridge courses are not part of the 30-semester credit hours required for the master’s degree. Grades for bridge courses are not included in a student’s GPA if the courses are taken before matriculation; they are included if completed after matriculation. Since bridge programs can be designed in a variety of ways, the graduate program director will assist students in planning and course selection.

Online Degree Information

The information technology and analytics MS program is designed to be completed part-time (one or two courses per term). Full-time options may be available with graduate program director's approval. Time to completion will depend on the student’s individual plan of study, when courses are offered, what electives are selected, and if the student takes a summer course. Advisors work closely with students after admission on course registration. Typically students finish this degree in two years. For specific details about the delivery format and learning experience, contact the program contact listed on this page. RIT does not offer student visas for online study.

Online Tuition Eligibility
The online Information Technology and Analytics MS is a designated online degree program that is billed at a 43% discount from our on-campus rate. View the current online tuition rate.

Online Study Restrictions for Some International Students

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 the Crimea region of the Ukraine, Cuba, Iran, North Korea, and Syria 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.

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