{"id":1991,"date":"2025-08-29T07:46:00","date_gmt":"2025-08-29T11:46:00","guid":{"rendered":"https:\/\/www.ramapo.edu\/dmc\/?p=1991"},"modified":"2025-08-29T07:46:00","modified_gmt":"2025-08-29T11:46:00","slug":"learn-how-ramapos-msds-aligns-with-the-nationally-recognized-adsa-core-data-science-competencies","status":"publish","type":"post","link":"https:\/\/www.ramapo.edu\/dmc\/2025\/08\/29\/learn-how-ramapos-msds-aligns-with-the-nationally-recognized-adsa-core-data-science-competencies\/","title":{"rendered":"Learn how Ramapo’s MSDS aligns with the nationally recognized ADSA core Data Science competencies"},"content":{"rendered":"
The Academic Data Science Alliance (ADSA) Data Science Taxonomy<\/a> represents a comprehensive framework of competencies for Master’s-level data science programs, developed through collaboration with leading academic institutions and federal partners including NSA, DOD, NIH, and NSF. <\/p>\n This nationally recognized taxonomy establishes standardized competencies that ensure graduates possess the critical skills needed in today’s data-driven economy, making it highly valued by employers across industries. <\/p>\n 秘密研究所’s Master of Science in Data Science program aligns exceptionally well with this prestigious framework, demonstrating our commitment to providing students with industry-relevant, federally-recognized competencies that will distinguish them in the competitive data science job market. It’s one of the reasons Ramapo’s MSDS has been consistently listed as one of Fortune’s Best Masters degrees in Data Science.<\/p>\n Our Master of Science (MS) in Data Science degree is a 30-credit program with course work in Python, R, Data Visualization, Database Systems, Machine Learning, Statistics and Mathematical Modeling. Full-time students will complete their degree in 18 months<\/b>. Courses are delivered as a combination of online, hybrid, and evening in-seat format – you can complete the degree while being on campus just one night a week<\/b>.<\/p>\n Explore the detailed mappings below to see how each course in our program contributes to building these essential, nationally-recognized data science skills.<\/p>\n Methodical approach to gather observations, measurements and information from different sources<\/p>\n<\/td>\n Process of using statistics to make conclusions about a population based on a random sample<\/p>\n<\/td>\n Method of generating sample data and making real-world predictions using statistical models<\/p>\n<\/td>\n Statistical techniques that simultaneously look at three or more variables<\/p>\n<\/td>\n Process of learning from data using statistical algorithms<\/p>\n<\/td>\n Theory based on Bayesian interpretation of probability<\/p>\n<\/td>\n Process of determining independent effect of a phenomenon<\/p>\n<\/td>\n Level of understanding of world representation for mathematical modeling<\/p>\n<\/td>\n Carrying out research in objective and controlled fashion<\/p>\n<\/td>\n Selection of subset from statistical population to estimate characteristics<\/p>\n<\/td>\n Fundamental mathematical concepts dealing with collections of objects and logical reasoning<\/p>\n<\/td>\n Mathematical structures and operations for solving systems of linear equations<\/p>\n<\/td>\n Mathematical techniques for finding the best solution from all feasible solutions<\/p>\n<\/td>\n Mathematical framework for analyzing random phenomena and uncertainty<\/p>\n<\/td>\n Basic mathematical operations and study of shapes, sizes, and properties of space<\/p>\n<\/td>\n Study of graphs as mathematical structures used to model pairwise relations<\/p>\n<\/td>\n Approach to analyzing data sets to summarize their main characteristics<\/p>\n<\/td>\n Description of how values of a variable are spread or distributed<\/p>\n<\/td>\n Graph using Cartesian coordinates to display values for two variables<\/p>\n<\/td>\n Statistical method used to evaluate the strength of relationship between variables<\/p>\n<\/td>\n Probability of an event occurring given that another event has occurred<\/p>\n<\/td>\n Examining locations, attributes, and relationships of features in spatial data<\/p>\n<\/td>\n Representation of data through graphics like charts, plots, infographics<\/p>\n<\/td>\n Technologies that enable computers to perform advanced functions including analysis<\/p>\n<\/td>\n Traditional artificial intelligence approaches using symbolic reasoning<\/p>\n<\/td>\n Contemporary AI approaches based on machine learning and data analysis<\/p>\n<\/td>\n Subfield of AI using data and algorithms to learn and improve accuracy over time<\/p>\n<\/td>\n Traditional machine learning algorithms and statistical methods<\/p>\n<\/td>\n Machine learning based on artificial neural networks with multiple processing layers<\/p>\n<\/td>\n Branch of AI allowing computers to interpret human language similarly to humans<\/p>\n<\/td>\n Assessment and representation of uncertainties in computational models<\/p>\n<\/td>\n Practice of analyzing large databases to generate new information<\/p>\n<\/td>\n Process of creating, testing, and implementing predictive models<\/p>\n<\/td>\n Identification and management of potential model failures<\/p>\n<\/td>\n Evaluation of model performance and reliability<\/p>\n<\/td>\n Representation of data through graphics like charts, plots, infographics<\/p>\n<\/td>\n Ways of organizing and storing data in computer programs<\/p>\n<\/td>\n Step-by-step procedures for solving computational problems<\/p>\n<\/td>\n Imitation of real-world processes or systems using computational models<\/p>\n<\/td>\n Practice of designing and building systems for collecting and analyzing data<\/p>\n<\/td>\n Process of producing detailed data models and database structures<\/p>\n<\/td>\n Process of detecting and correcting corrupt or inaccurate records<\/p>\n<\/td>\n Management and preservation of data records over time<\/p>\n<\/td>\n Technologies for processing data sets too large for traditional software<\/p>\n<\/td>\n Protection of data from unauthorized access and ensuring privacy<\/p>\n<\/td>\n Delivery of computing services over the internet<\/p>\n<\/td>\n Use of parallel processing for running advanced computation programs<\/p>\n<\/td>\n Process of creating computer programs using programming languages<\/p>\n<\/td>\n Tools and practices for team software development<\/p>\n<\/td>\n Systems for storing and managing large amounts of structured data<\/p>\n<\/td>\n Understanding the broader impact and consequences of data analysis<\/p>\n<\/td>\n Creating user-centered design for data products and interfaces<\/p>\n<\/td>\n Communicating insights and findings through compelling data narratives<\/p>\n<\/td>\n Design approach that focuses on human needs and experiences<\/p>\n<\/td>\n Formulating testable predictions based on observations<\/p>\n<\/td>\n Crafting questions that can be answered through data analysis<\/p>\n<\/td>\n Application of logical reasoning in computational problem-solving<\/p>\n<\/td>\n Making decisions based on data analysis rather than intuition<\/p>\n<\/td>\n Complete process from data collection to research conclusions<\/p>\n<\/td>\n Communicating analytical findings to support decision-making<\/p>\n<\/td>\n\n
\n Foundations of Analytics: Statistics<\/th>\n<\/tr>\n \n \n Data Collection Design<\/h4>\n
\n \n
\n \n Inference<\/h4>\n
\n \n
\n \n Modeling (Stochastic)<\/h4>\n
\n \n
\n \n Multivariate Analysis<\/h4>\n
\n \n
\n \n Statistical Learning<\/h4>\n
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\n \n Bayesian Methods<\/h4>\n
\n \n
\n \n Causal inference<\/h4>\n
\n \n
\n \n Model uncertainty<\/h4>\n
\n \n
\n \n Experimental design<\/h4>\n
\n \n
\n \n Sampling<\/h4>\n
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\n Foundations of Analytics: Mathematics<\/th>\n<\/tr>\n \n \n Set theory and basic logic<\/h4>\n
\n \n
\n \n Matrices and basic linear algebra<\/h4>\n
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\n \n Optimization – algorithm<\/h4>\n
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\n \n Probability theory<\/h4>\n
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\n \n Arithmetic and Geometry<\/h4>\n
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\n \n Graph Theory and Networks<\/h4>\n
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\n Foundations of Analytics: Data Analytics<\/th>\n<\/tr>\n \n \n Exploratory Analysis<\/h4>\n
\n \n
\n \n Variable Distributions<\/h4>\n
\n \n
\n \n Scatter Plots<\/h4>\n
\n \n
\n \n Correlation Analysis<\/h4>\n
\n \n
\n \n Conditional Probability<\/h4>\n
\n \n
\n \n Spatial Analysis<\/h4>\n
\n \n
\n \n Data Visualization<\/h4>\n
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\n \n Artificial Intelligence<\/h4>\n
\n \n
\n \n Classical AI<\/h4>\n
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\n \n Modern AI\/Data Driven AI<\/h4>\n
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\n \n Machine Learning<\/h4>\n
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\n \n Classical ML<\/h4>\n
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\n \n Deep Learning<\/h4>\n
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\n \n NLP<\/h4>\n
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\n \n Uncertainty Quantification\/Characterization<\/h4>\n
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\n \n Data Mining<\/h4>\n
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\n Foundations of Analytics: Data Modeling<\/th>\n<\/tr>\n \n \n Model Development and Deployment<\/h4>\n
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\n \n Model Risks and Mitigation Strategies<\/h4>\n
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\n \n Model analysis and Validation<\/h4>\n
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\n \n Data Visualization<\/h4>\n
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\n Systems and Implementation: Computing and Computer Fundamentals<\/th>\n<\/tr>\n \n \n Data Structures<\/h4>\n
\n \n
\n \n Algorithms<\/h4>\n
\n \n
\n \n Simulations<\/h4>\n
\n \n
\n \n Data Engineering<\/h4>\n
\n \n
\n \n Database Design<\/h4>\n
\n \n
\n \n Data Preparation and Cleaning<\/h4>\n
\n \n
\n \n Records Retention and Curation<\/h4>\n
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\n \n Big Data Systems<\/h4>\n
\n \n
\n \n Data Security and Privacy<\/h4>\n
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\n \n Cloud Computing<\/h4>\n
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\n \n High Performance Computing<\/h4>\n
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\n Systems and Implementation: Software Development and Maintenance<\/th>\n<\/tr>\n \n \n Programming<\/h4>\n
\n \n
\n \n Collaboration and version control<\/h4>\n
\n \n
\n \n Database\/data warehousing<\/h4>\n
\n \n
\n Data Science Project Design: Users and Impacted Groups<\/th>\n<\/tr>\n \n \n Implications of analysis and results<\/h4>\n
\n \n
\n \n Defining the user and UX design<\/h4>\n
\n \n
\n \n Story-telling with data<\/h4>\n
\n \n
\n \n Human-centered design<\/h4>\n
\n \n
\n Data Science Project Design: Research Methods<\/th>\n<\/tr>\n \n \n Hypothesis development<\/h4>\n
\n \n
\n \n Defining data-driven questions<\/h4>\n
\n \n
\n \n Computational logic<\/h4>\n
\n \n
\n \n Data-driven decision making<\/h4>\n
\n \n
\n \n Data\/research lifecycle<\/h4>\n
\n \n
\n \n Analysis and presentation of decisions<\/h4>\n
\n \n
\n