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Master of Science in Data Scientific discipline

Format: Online

Application Deadlines

• Fall 2022 Regular Deadline Extension: June 16, 2022

The MS in Data Science (previously MS in Information Analytics) online degree program helps students earn the credentials and acquire the skills needed to enter or advance in the fast-growing field of data science. Ranked terminal year as i of the Best Value Online Big Information Programs, the MS in Data Scientific discipline online degree programme offers foundational noesis and hands-on programming competencies, resulting in project-based work samples similar to that of a programming boot camp.

The plan's learning objectives and demanding, easily-on courses are designed around employer needs. Throughout their time in the program, students build portfolios of increasingly complex projects using pop programming languages such as R and Python, which mirror the electric current experience and demands of the IT workplace. Students build predictive and prescriptive models, practice giving presentations, and review each other's work in a user-friendly online setting, ensuring that they are equipped with the expertise almost valued in today's marketplace. The MS in Information Science program culminates with a capstone project that represents highly sophisticated, but practical, solutions to accost real world issues.

Additionally, the program'south faculty comprise committed and engaged engineering practitioners who are experts in their fields.  They invest fourth dimension in edifice courses on the use of open source best-practice tools that satisfy high employer demands for quality programming and employ of advanced techniques.

Career Prospects

The MS in Information Science program prepares graduates for a multifariousness of technical and managerial positions, such as data scientist, business organization intelligence analyst, knowledge engineer, informatics engineer, data annotator, data mining engineer, and data warehousing manager.

Admissions Criteria

Applicants must possess a available's degree from an accredited institution, with a GPA of 3.0 or college on a 4.0 calibration. Applicants are required to write a personal statement, upload a resume, and provide two letters of recommendation. Letters of recommendation may be submitted before or later on submitting an awarding. Please notation that an individual interview may exist necessary.

Equally an interdisciplinary field, we welcome applicants from diverse professional backgrounds. However, because the MS in Data Science is a highly quantitative and technical major as compared with MBA-like programs, acceptance requires applicants to demonstrate current skills in:

1. Statistics and probability including descriptive statistics, skewness/kurtosis, histograms, statistical error, correlation, single variable linear regression assay, significance testing, probability distributions, and basic probability modeling;
2. Linear algebra including basic matrix manipulation, dot and cross products, inverse matrices, eigenvalues, representing problems every bit matrices, and solving small systems of linear equations;
3. Programming in a high-level language such as Python, Java, JavaScript, C++, C, Ruby, or SAS (ii+ years). Applicants must be able to write working code from scratch;
4. Relational databases including connecting to and manipulating information, working with tables, joins, bones relational algebra, and SQL queries. Two or more than years of experience with Microsoft Admission can be substituted if the applicant is able to perform the same operations without using Access's graphical interface; and,
5. Analytical thinking including the ability to interpret real-globe phenomena into quantitative representations and, conversely, the power to interpret quantitative representations with practical explanations.

Skills in these areas will be assessed in 2 ways:

1. Completion of credit-bearing coursework with a course of B or ameliorate from an accredited higher or university OR 2+ years of relevant experience on a resume; and,
2. Completion of a mandatory claiming exam that will appraise current skill and noesis in these areas.If you lack the skills required for admission to the program and/or are unable to answer the questions found in the challenge test, please email d atascience@sps.cuny.edu for recommendations on how to pick up the necessary skill sets.

Bridge Plan

If you lot have completed credit-bearing courses in the above areas or have used these skills at work but are no longer skilful, we offering three bridge courses: R Programming, SQL, and Information Science Math. These bridge courses are intended to refresh cognition and skills, but are not for individuals who are learning these topics for the first time.

For questions, please email datascience@sps.cuny.edu.

Application Deadlines

• Fall 2022 Regular Borderline Extension: June 16, 2022

Apply Now

Student/Alumni Profiles

Duubar E. Villalobos Jimenez

MS in Information Scientific discipline 2019

"Since my master's program was 100% online, I had the feel as to how to cope with online piece of work action. When COVID hit, and everything became remote, I was able to switch gears seamlessly."

James Hamski

MS in Data Analytics 2017

"It's not just about perception, I can "walk the walk" and produce results because of the skills I gained."

Jonathan Hernandez

MS in Information Science

"The most enjoying aspect of the program is the fact that we get to piece of work on existent-globe programs and can apply our skills learned in these courses to solve existent-earth information scientific discipline issues."

Youqing Xiang

MS in Data Science

"There are 4 key success factors for a information analyst: figurer programming and mathematical skills, domain knowledge, communication, and teamwork. CUNY SPS has prepared me in all of these areas."

Recent News About Master of Science in Data Science

Enhancing Effective Didactics and Learning Using Assessment Data

February 25, 2022

Information Age Publishing

Dr. Jason Bryer, banana professor and associate director of the data scientific discipline and information systems programs at CUNY SPS, has co-written a chapter in the book "Enhancing Effective Instruction and Learning Using Assessment Information" available now through Information Historic period Publishing. The chapter, entitled "The Use of Predictive Modeling for Assessing College Readiness," introduces software for automating some aspects of developmental teaching and the apply of predictive modeling.

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Source: https://sps.cuny.edu/academics/graduate/master-science-data-science-ms