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 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.
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.
Data science, MS degree, typical course sequence
Sem. Cr. Hrs.
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.
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.
Software Engineering for Data Science
Data Science 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.
Total Semester Credit Hours
To be considered for admission to the MS in data science, candidates must fulfill the following requirements:
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.
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.
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