Academic Director: Arthur O’Connor, PhD
CUNY School of Professional Studies
101 West 31st Street, 7th Floor
New York, NY 10001
Email Contact: email@example.com
The MS in Data Analytics and BS in Information Systems on-line degree programs at the CUNY School of Professional Studies offer students the foundational knowledge, practical skills, hands-on technical experience, and academic credentials in order to enter or advance their careers in the fast-growing fields of data science and information technology.
Program Learning Outcomes
Graduates of the MS in Data Science will be able to:
- Data Acquisition, Management and Programming: use industry standard data science and analytics packages to collect, describe, clean, format, model, explore and verify structured data, unstructured data and big data.
- Foundational Math and Statistics: demonstrate understanding of linear algebra - differential equations, linear and non-linear programming (NLP), algorithmic search methods for optimization, integer programming (IP) - probability, Bayesian statistics, univariate and multivariate calculus.
- Modeling: Use statistical and machine learning modeling techniques to design, build and test/assess models.
- Dissemination: Develop/write/present reports to explain/present their models, results, and analyses in plain and easy-to-understand language.
The MS in Data Science 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.
Click HERE for an overview of the program.
Ranked last year as one of the top Master’s in Data Science worldwide as well as one of the Best Value Online Big Data Programs, the MS in Data Science online degree program offers foundational knowledge and hands-on programming competencies, resulting in project-based work samples similar to that of a programming boot camp.
The program’s learning objectives and demanding courses are designed around employer needs. Throughout their time in the program, students build portfolios of increasingly complex projects using popular programming languages such as R and Python, which mirror the current experiences and demands of the IT workplace. Students build predictive and prescriptive models, practice giving presentations, and review each other’s work in a convenient online setting, which ensures that they are equipped with the expertise most valued in today’s marketplace. The MS in Data Science program culminates with a capstone project that represents highly sophisticated, but practical, solutions to address real problems.
Additionally, the program’s faculty comprise committed and engaged technology practitioners who are experts in their fields. They invest time in building courses on the use of open source best-practice tools that satisfy high employer demands for quality programming and use of advanced techniques.
Applicants must possess a bachelor’s degree from an accredited institution. An undergraduate performance of a GPA of 3.0 or higher on a 4.0 scale is preferred. Applicants are required to write a personal statement, upload a resume, and provide two letters of recommendation.
As part of the admissions process, we also require completion of a short “challenge exam,” which is used to assess candidates’ proficiency in three areas: applied math, R and SQL programming.
For those candidates who demonstrate a basic level of competency on their challenge exam, but are assessed to need some remedial help in order to succeed in the program, three on-line “bridge” workshops - in applied math, R programming, and SQL - are offered for free to help them brush up on their skills.
Completion and passing of the assigned workshops are required for such “conditionally-accepted” applicants to be admitted into the MSDS degree program.
The three free online workshops are open to both fully- and conditionally-admitted applicants.
As an interdisciplinary field, we welcome applicants from diverse professional backgrounds. However, because the MS in Data Science is a highly quantitative and technical field , acceptance requires applicants to demonstrate current skills in:
- Statistics and probability including descriptive statistics, skewness/kurtosis, histograms, statistical error, correlation, single variable linear regression analysis, significance testing, probability distributions, and basic probability modeling;
- Linear algebra including basic matrix manipulation, dot and cross products, inverse matrices, eigenvalues, representing problems as matrices, and solving small systems of linear equations;
- Relational databases including connecting to and manipulating data, working with tables, joins, basic relational algebra, and SQL queries; and,
- Analytical thinking including the ability to translate real-world phenomena into quantitative representations and, conversely, the ability to interpret quantitative representations with practical explanations.