{"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

Ramapo College’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

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Apply Now <\/i><\/a><\/div>\n
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Request Information <\/i><\/a><\/div>\n<\/div>\n\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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Foundations of Analytics: Statistics<\/th>\n<\/tr>\n
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Data Collection Design<\/h4>\n

Methodical approach to gather observations, measurements and information from different sources<\/p>\n<\/td>\n

\n
    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Inference<\/h4>\n

Process of using statistics to make conclusions about a population based on a random sample<\/p>\n<\/td>\n

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    \n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Modeling (Stochastic)<\/h4>\n

Method of generating sample data and making real-world predictions using statistical models<\/p>\n<\/td>\n

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    \n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 687 – TIME SERIES DATA<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Multivariate Analysis<\/h4>\n

Statistical techniques that simultaneously look at three or more variables<\/p>\n<\/td>\n

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    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • MATH 562 – APPLIED LINEAR ALGEBRA<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Statistical Learning<\/h4>\n

Process of learning from data using statistical algorithms<\/p>\n<\/td>\n

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    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • MATH 562 – APPLIED LINEAR ALGEBRA<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Bayesian Methods<\/h4>\n

Theory based on Bayesian interpretation of probability<\/p>\n<\/td>\n

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    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Causal inference<\/h4>\n

Process of determining independent effect of a phenomenon<\/p>\n<\/td>\n

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    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Model uncertainty<\/h4>\n

Level of understanding of world representation for mathematical modeling<\/p>\n<\/td>\n

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    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Experimental design<\/h4>\n

Carrying out research in objective and controlled fashion<\/p>\n<\/td>\n

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    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Sampling<\/h4>\n

Selection of subset from statistical population to estimate characteristics<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Foundations of Analytics: Mathematics<\/th>\n<\/tr>\n
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Set theory and basic logic<\/h4>\n

Fundamental mathematical concepts dealing with collections of objects and logical reasoning<\/p>\n<\/td>\n

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    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • MATH 540 – CRYPTOGRAPHY<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Matrices and basic linear algebra<\/h4>\n

Mathematical structures and operations for solving systems of linear equations<\/p>\n<\/td>\n

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    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Optimization – algorithm<\/h4>\n

Mathematical techniques for finding the best solution from all feasible solutions<\/p>\n<\/td>\n

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    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • CMPS 531 – DATA STRUCTURES AND ALGORITHMS<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • CMPS 645 – ANALYSIS OF ALGORITHMS<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Probability theory<\/h4>\n

Mathematical framework for analyzing random phenomena and uncertainty<\/p>\n<\/td>\n

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    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Arithmetic and Geometry<\/h4>\n

Basic mathematical operations and study of shapes, sizes, and properties of space<\/p>\n<\/td>\n

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    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • MATH 540 – CRYPTOGRAPHY<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Graph Theory and Networks<\/h4>\n

Study of graphs as mathematical structures used to model pairwise relations<\/p>\n<\/td>\n

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    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • CMPS 531 – DATA STRUCTURES AND ALGORITHMS<\/li>\n
  • CMPS 645 – ANALYSIS OF ALGORITHMS<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Foundations of Analytics: Data Analytics<\/th>\n<\/tr>\n
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Exploratory Analysis<\/h4>\n

Approach to analyzing data sets to summarize their main characteristics<\/p>\n<\/td>\n

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    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • DATA 687 – TIME SERIES DATA<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Variable Distributions<\/h4>\n

Description of how values of a variable are spread or distributed<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Scatter Plots<\/h4>\n

Graph using Cartesian coordinates to display values for two variables<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Correlation Analysis<\/h4>\n

Statistical method used to evaluate the strength of relationship between variables<\/p>\n<\/td>\n

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    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Conditional Probability<\/h4>\n

Probability of an event occurring given that another event has occurred<\/p>\n<\/td>\n

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    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Spatial Analysis<\/h4>\n

Examining locations, attributes, and relationships of features in spatial data<\/p>\n<\/td>\n

\n
    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Data Visualization<\/h4>\n

Representation of data through graphics like charts, plots, infographics<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • DATA 687 – TIME SERIES DATA<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Artificial Intelligence<\/h4>\n

Technologies that enable computers to perform advanced functions including analysis<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Classical AI<\/h4>\n

Traditional artificial intelligence approaches using symbolic reasoning<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Modern AI\/Data Driven AI<\/h4>\n

Contemporary AI approaches based on machine learning and data analysis<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Machine Learning<\/h4>\n

Subfield of AI using data and algorithms to learn and improve accuracy over time<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Classical ML<\/h4>\n

Traditional machine learning algorithms and statistical methods<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Deep Learning<\/h4>\n

Machine learning based on artificial neural networks with multiple processing layers<\/p>\n<\/td>\n

\n
    \n
  • CMPS 620 – MACHINE LEARNING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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NLP<\/h4>\n

Branch of AI allowing computers to interpret human language similarly to humans<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Uncertainty Quantification\/Characterization<\/h4>\n

Assessment and representation of uncertainties in computational models<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data Mining<\/h4>\n

Practice of analyzing large databases to generate new information<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Foundations of Analytics: Data Modeling<\/th>\n<\/tr>\n
\n

Model Development and Deployment<\/h4>\n

Process of creating, testing, and implementing predictive models<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Model Risks and Mitigation Strategies<\/h4>\n

Identification and management of potential model failures<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Model analysis and Validation<\/h4>\n

Evaluation of model performance and reliability<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data Visualization<\/h4>\n

Representation of data through graphics like charts, plots, infographics<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Systems and Implementation: Computing and Computer Fundamentals<\/th>\n<\/tr>\n
\n

Data Structures<\/h4>\n

Ways of organizing and storing data in computer programs<\/p>\n<\/td>\n

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    \n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE<\/li>\n
  • CMPS 531 – DATA STRUCTURES AND ALGORITHMS<\/li>\n
  • CMPS 645 – ANALYSIS OF ALGORITHMS<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
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Algorithms<\/h4>\n

Step-by-step procedures for solving computational problems<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • MATH 540 – CRYPTOGRAPHY<\/li>\n
  • CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE<\/li>\n
  • CMPS 531 – DATA STRUCTURES AND ALGORITHMS<\/li>\n
  • CMPS 645 – ANALYSIS OF ALGORITHMS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Simulations<\/h4>\n

Imitation of real-world processes or systems using computational models<\/p>\n<\/td>\n

\n
    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 687 – TIME SERIES DATA<\/li>\n
  • MATH 654 – APPLIED PROBABILITY<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data Engineering<\/h4>\n

Practice of designing and building systems for collecting and analyzing data<\/p>\n<\/td>\n

\n
    \n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Database Design<\/h4>\n

Process of producing detailed data models and database structures<\/p>\n<\/td>\n

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    \n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data Preparation and Cleaning<\/h4>\n

Process of detecting and correcting corrupt or inaccurate records<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 687 – TIME SERIES DATA<\/li>\n
  • CMPS 620 – MACHINE LEARNING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Records Retention and Curation<\/h4>\n

Management and preservation of data records over time<\/p>\n<\/td>\n

\n
    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Big Data Systems<\/h4>\n

Technologies for processing data sets too large for traditional software<\/p>\n<\/td>\n

\n
    \n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data Security and Privacy<\/h4>\n

Protection of data from unauthorized access and ensuring privacy<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 540 – CRYPTOGRAPHY<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Cloud Computing<\/h4>\n

Delivery of computing services over the internet<\/p>\n<\/td>\n

\n
    \n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

High Performance Computing<\/h4>\n

Use of parallel processing for running advanced computation programs<\/p>\n<\/td>\n

\n
    \n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n
  • CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Systems and Implementation: Software Development and Maintenance<\/th>\n<\/tr>\n
\n

Programming<\/h4>\n

Process of creating computer programs using programming languages<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • MATH 540 – CRYPTOGRAPHY<\/li>\n
  • CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE<\/li>\n
  • CMPS 531 – DATA STRUCTURES AND ALGORITHMS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Collaboration and version control<\/h4>\n

Tools and practices for team software development<\/p>\n<\/td>\n

\n
    \n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n
  • CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Database\/data warehousing<\/h4>\n

Systems for storing and managing large amounts of structured data<\/p>\n<\/td>\n

\n
    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Data Science Project Design: Users and Impacted Groups<\/th>\n<\/tr>\n
\n

Implications of analysis and results<\/h4>\n

Understanding the broader impact and consequences of data analysis<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Defining the user and UX design<\/h4>\n

Creating user-centered design for data products and interfaces<\/p>\n<\/td>\n

\n
    \n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Story-telling with data<\/h4>\n

Communicating insights and findings through compelling data narratives<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Human-centered design<\/h4>\n

Design approach that focuses on human needs and experiences<\/p>\n<\/td>\n

\n
    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Data Science Project Design: Research Methods<\/th>\n<\/tr>\n
\n

Hypothesis development<\/h4>\n

Formulating testable predictions based on observations<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • CMPS 531 – DATA STRUCTURES AND ALGORITHMS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Defining data-driven questions<\/h4>\n

Crafting questions that can be answered through data analysis<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • DATA 687 – TIME SERIES DATA<\/li>\n
  • CMPS 531 – DATA STRUCTURES AND ALGORITHMS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Computational logic<\/h4>\n

Application of logical reasoning in computational problem-solving<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data-driven decision making<\/h4>\n

Making decisions based on data analysis rather than intuition<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • DATA 687 – TIME SERIES DATA<\/li>\n
  • CMPS 531 – DATA STRUCTURES AND ALGORITHMS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data\/research lifecycle<\/h4>\n

Complete process from data collection to research conclusions<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Analysis and presentation of decisions<\/h4>\n

Communicating analytical findings to support decision-making<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Data Science Project Design: Data<\/th>\n<\/tr>\n
\n

Data acquisition<\/h4>\n

Process of gathering data from various sources<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data governance<\/h4>\n

Management of data availability, usability, integrity and security<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data provenance and citation<\/h4>\n

Documentation of data sources and proper attribution<\/p>\n<\/td>\n

\n
    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Data Science Project Design: Open Science by Design<\/th>\n<\/tr>\n
\n

Reproducibility, replicability, repeatability<\/h4>\n

Ensuring research can be verified and repeated<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Interactive computing<\/h4>\n

Computing environment that allows real-time user interaction<\/p>\n<\/td>\n

\n
    \n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Data Science Project Design: Visualization<\/th>\n<\/tr>\n
\n

Grammar of graphics<\/h4>\n

System for describing and building statistical graphics<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Static and dynamic visualization design<\/h4>\n

Creating both fixed and interactive data visualizations<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • CMPS 530 – PYTHON FOR DATA SCIENCE<\/li>\n
  • MATH 570 – APPLIED STATISTICS<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 670 – DATA VISUALIZATION<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Data Science In Practice: Responsible Practices<\/th>\n<\/tr>\n
\n

Relevant domain knowledge for effective decision-making<\/h4>\n

Understanding the specific field or industry context<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Legal considerations<\/h4>\n

Understanding legal requirements and constraints in data use<\/p>\n<\/td>\n

\n
    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data privacy<\/h4>\n

Protection of personal and sensitive information in datasets<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • MATH 540 – CRYPTOGRAPHY<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data and product\/system security and resilience<\/h4>\n

Ensuring robust protection against threats<\/p>\n<\/td>\n

\n
    \n
  • MATH 540 – CRYPTOGRAPHY<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Data and product\/system governance<\/h4>\n

Oversight and management of data systems<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Research integrity<\/h4>\n

Adherence to ethical principles in research conduct<\/p>\n<\/td>\n

\n
    \n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Assessment, monitoring, and management of risks<\/h4>\n

Systematic approach to identifying and controlling risks<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Understanding and uncovering bias<\/h4>\n

Identifying and addressing systematic errors in data and analysis<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Interpretability and Explainability<\/h4>\n

Making complex models understandable to humans<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Human impacts of design<\/h4>\n

Considering how design decisions affect people and communities<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Responsible data collection<\/h4>\n

Ethical approaches to gathering data from individuals and communities<\/p>\n<\/td>\n

\n
    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Understanding impacted communities<\/h4>\n

Recognizing how data work affects different groups of people<\/p>\n<\/td>\n

\n
    \n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Data Science In Practice: Effective Collaboration<\/th>\n<\/tr>\n
\n

Working with stakeholders<\/h4>\n

Collaborating effectively with various project participants<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Working with domain experts<\/h4>\n

Partnering with subject matter experts<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • DATA 620 – ETHICS IN DATA AND COMPUTING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Project management<\/h4>\n

Planning, executing, and controlling project activities<\/p>\n<\/td>\n

\n
    \n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Infrastructure cost and benefits<\/h4>\n

Evaluating financial and operational aspects of technology infrastructure<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Participatory research \/ stakeholder engagement<\/h4>\n

Including community members in research processes<\/p>\n<\/td>\n

\n
    \n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Data Science In Practice: Communication<\/th>\n<\/tr>\n
\n

Technical writing skills<\/h4>\n

Communicating complex technical information clearly<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Communication (oral) and presentation skills<\/h4>\n

Effectively presenting information to audiences<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • CMPS 664 – BIG DATA AND DATABASE DESIGN<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
\n

Documentation<\/h4>\n

Creating clear and comprehensive project documentation<\/p>\n<\/td>\n

\n
    \n
  • DATA 601 – INTRODUCTION TO DATA SCIENCE<\/li>\n
  • MATH 680 – ADVANCED MATHEMATICAL MODELING<\/li>\n
  • DATA 745\/750 – DATA SCIENCE THESIS<\/li>\n
  • DATA 730 – FIELDWORK<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

    The Academic Data Science Alliance (ADSA) Data Science Taxonomy 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. This nationally recognized taxonomy establishes standardized competencies that ensure graduates possess the critical skills needed in today’s data-driven economy, […]<\/p>\n","protected":false},"author":214,"featured_media":1500,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,4],"tags":[],"class_list":["post-1991","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","category-msds"],"yoast_head":"\nLearn how Ramapo's MSDS aligns with the nationally recognized ADSA core Data Science competencies - Center for Data, Mathematical and Computational Sciences<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ramapo.edu\/dmc\/2025\/08\/29\/learn-how-ramapos-msds-aligns-with-the-nationally-recognized-adsa-core-data-science-competencies\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Learn how Ramapo's MSDS aligns with the nationally recognized ADSA core Data Science competencies\" \/>\n<meta property=\"og:description\" content=\"The Academic Data Science Alliance (ADSA) Data Science Taxonomy 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. 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