Center for Data, Mathematical, and Computational Sciences

August 29, 2025Learn how Ramapo's MSDS aligns with the nationally recognized ADSA core Data Science competencies

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, making it highly valued by employers across industries.

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.

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. 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.

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Explore the detailed mappings below to see how each course in our program contributes to building these essential, nationally-recognized data science skills.

Foundations of Analytics: Statistics
Data Collection Design Methodical approach to gather observations, measurements and information from different sources	DATA 620 – ETHICS IN DATA AND COMPUTING DATA 670 – DATA VISUALIZATION MATH 654 – APPLIED PROBABILITY
Inference Process of using statistics to make conclusions about a population based on a random sample	MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING
Modeling (Stochastic) Method of generating sample data and making real-world predictions using statistical models	MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 687 – TIME SERIES DATA MATH 654 – APPLIED PROBABILITY
Multivariate Analysis Statistical techniques that simultaneously look at three or more variables	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 670 – DATA VISUALIZATION MATH 562 – APPLIED LINEAR ALGEBRA CMPS 620 – MACHINE LEARNING
Statistical Learning Process of learning from data using statistical algorithms	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING MATH 562 – APPLIED LINEAR ALGEBRA CMPS 620 – MACHINE LEARNING
Bayesian Methods Theory based on Bayesian interpretation of probability	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING
Causal inference Process of determining independent effect of a phenomenon	DATA 620 – ETHICS IN DATA AND COMPUTING MATH 570 – APPLIED STATISTICS
Model uncertainty Level of understanding of world representation for mathematical modeling	MATH 680 – ADVANCED MATHEMATICAL MODELING MATH 654 – APPLIED PROBABILITY
Experimental design Carrying out research in objective and controlled fashion	DATA 620 – ETHICS IN DATA AND COMPUTING
Sampling Selection of subset from statistical population to estimate characteristics	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING
Foundations of Analytics: Mathematics
Set theory and basic logic Fundamental mathematical concepts dealing with collections of objects and logical reasoning	MATH 680 – ADVANCED MATHEMATICAL MODELING MATH 540 – CRYPTOGRAPHY
Matrices and basic linear algebra Mathematical structures and operations for solving systems of linear equations	MATH 680 – ADVANCED MATHEMATICAL MODELING MATH 654 – APPLIED PROBABILITY CMPS 620 – MACHINE LEARNING
Optimization – algorithm Mathematical techniques for finding the best solution from all feasible solutions	MATH 680 – ADVANCED MATHEMATICAL MODELING CMPS 531 – DATA STRUCTURES AND ALGORITHMS CMPS 620 – MACHINE LEARNING CMPS 645 – ANALYSIS OF ALGORITHMS
Probability theory Mathematical framework for analyzing random phenomena and uncertainty	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 680 – ADVANCED MATHEMATICAL MODELING
Arithmetic and Geometry Basic mathematical operations and study of shapes, sizes, and properties of space	MATH 680 – ADVANCED MATHEMATICAL MODELING MATH 540 – CRYPTOGRAPHY
Graph Theory and Networks Study of graphs as mathematical structures used to model pairwise relations	MATH 680 – ADVANCED MATHEMATICAL MODELING CMPS 531 – DATA STRUCTURES AND ALGORITHMS CMPS 645 – ANALYSIS OF ALGORITHMS
Foundations of Analytics: Data Analytics
Exploratory Analysis Approach to analyzing data sets to summarize their main characteristics	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 530 – PYTHON FOR DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 670 – DATA VISUALIZATION DATA 687 – TIME SERIES DATA CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Variable Distributions Description of how values of a variable are spread or distributed	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 670 – DATA VISUALIZATION MATH 654 – APPLIED PROBABILITY CMPS 620 – MACHINE LEARNING
Scatter Plots Graph using Cartesian coordinates to display values for two variables	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 530 – PYTHON FOR DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 670 – DATA VISUALIZATION CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Correlation Analysis Statistical method used to evaluate the strength of relationship between variables	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 670 – DATA VISUALIZATION CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Conditional Probability Probability of an event occurring given that another event has occurred	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING MATH 654 – APPLIED PROBABILITY DATA 730 – FIELDWORK
Spatial Analysis Examining locations, attributes, and relationships of features in spatial data	MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 670 – DATA VISUALIZATION
Data Visualization Representation of data through graphics like charts, plots, infographics	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 530 – PYTHON FOR DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 670 – DATA VISUALIZATION DATA 687 – TIME SERIES DATA MATH 654 – APPLIED PROBABILITY CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Artificial Intelligence Technologies that enable computers to perform advanced functions including analysis	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING
Classical AI Traditional artificial intelligence approaches using symbolic reasoning	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 620 – MACHINE LEARNING
Modern AI/Data Driven AI Contemporary AI approaches based on machine learning and data analysis	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 620 – MACHINE LEARNING
Machine Learning Subfield of AI using data and algorithms to learn and improve accuracy over time	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Classical ML Traditional machine learning algorithms and statistical methods	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Deep Learning Machine learning based on artificial neural networks with multiple processing layers	CMPS 620 – MACHINE LEARNING
NLP Branch of AI allowing computers to interpret human language similarly to humans	DATA 601 – INTRODUCTION TO DATA SCIENCE CMPS 530 – PYTHON FOR DATA SCIENCE
Uncertainty Quantification/Characterization Assessment and representation of uncertainties in computational models	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 730 – FIELDWORK
Data Mining Practice of analyzing large databases to generate new information	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 530 – PYTHON FOR DATA SCIENCE CMPS 664 – BIG DATA AND DATABASE DESIGN MATH 680 – ADVANCED MATHEMATICAL MODELING CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Foundations of Analytics: Data Modeling
Model Development and Deployment Process of creating, testing, and implementing predictive models	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 680 – ADVANCED MATHEMATICAL MODELING CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Model Risks and Mitigation Strategies Identification and management of potential model failures	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 730 – FIELDWORK
Model analysis and Validation Evaluation of model performance and reliability	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 730 – FIELDWORK
Data Visualization Representation of data through graphics like charts, plots, infographics	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 530 – PYTHON FOR DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 670 – DATA VISUALIZATION MATH 654 – APPLIED PROBABILITY CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Systems and Implementation: Computing and Computer Fundamentals
Data Structures Ways of organizing and storing data in computer programs	CMPS 530 – PYTHON FOR DATA SCIENCE CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE CMPS 531 – DATA STRUCTURES AND ALGORITHMS CMPS 645 – ANALYSIS OF ALGORITHMS
Algorithms Step-by-step procedures for solving computational problems	DATA 601 – INTRODUCTION TO DATA SCIENCE CMPS 530 – PYTHON FOR DATA SCIENCE MATH 680 – ADVANCED MATHEMATICAL MODELING MATH 540 – CRYPTOGRAPHY CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE CMPS 531 – DATA STRUCTURES AND ALGORITHMS CMPS 645 – ANALYSIS OF ALGORITHMS DATA 730 – FIELDWORK
Simulations Imitation of real-world processes or systems using computational models	DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 530 – PYTHON FOR DATA SCIENCE MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 687 – TIME SERIES DATA MATH 654 – APPLIED PROBABILITY DATA 730 – FIELDWORK
Data Engineering Practice of designing and building systems for collecting and analyzing data	CMPS 530 – PYTHON FOR DATA SCIENCE CMPS 664 – BIG DATA AND DATABASE DESIGN
Database Design Process of producing detailed data models and database structures	CMPS 664 – BIG DATA AND DATABASE DESIGN
Data Preparation and Cleaning Process of detecting and correcting corrupt or inaccurate records	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 530 – PYTHON FOR DATA SCIENCE CMPS 664 – BIG DATA AND DATABASE DESIGN MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 687 – TIME SERIES DATA CMPS 620 – MACHINE LEARNING DATA 730 – FIELDWORK
Records Retention and Curation Management and preservation of data records over time	DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 664 – BIG DATA AND DATABASE DESIGN
Big Data Systems Technologies for processing data sets too large for traditional software	CMPS 664 – BIG DATA AND DATABASE DESIGN
Data Security and Privacy Protection of data from unauthorized access and ensuring privacy	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 540 – CRYPTOGRAPHY
Cloud Computing Delivery of computing services over the internet	CMPS 664 – BIG DATA AND DATABASE DESIGN
High Performance Computing Use of parallel processing for running advanced computation programs	CMPS 664 – BIG DATA AND DATABASE DESIGN CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE
Systems and Implementation: Software Development and Maintenance
Programming Process of creating computer programs using programming languages	DATA 601 – INTRODUCTION TO DATA SCIENCE CMPS 530 – PYTHON FOR DATA SCIENCE CMPS 664 – BIG DATA AND DATABASE DESIGN MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING MATH 540 – CRYPTOGRAPHY CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE CMPS 531 – DATA STRUCTURES AND ALGORITHMS DATA 730 – FIELDWORK
Collaboration and version control Tools and practices for team software development	CMPS 530 – PYTHON FOR DATA SCIENCE CMPS 664 – BIG DATA AND DATABASE DESIGN CMPS 547 – FOUNDATIONS OF COMPUTER SCIENCE
Database/data warehousing Systems for storing and managing large amounts of structured data	DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 664 – BIG DATA AND DATABASE DESIGN
Data Science Project Design: Users and Impacted Groups
Implications of analysis and results Understanding the broader impact and consequences of data analysis	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 730 – FIELDWORK
Defining the user and UX design Creating user-centered design for data products and interfaces	CMPS 530 – PYTHON FOR DATA SCIENCE MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 730 – FIELDWORK
Story-telling with data Communicating insights and findings through compelling data narratives	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Human-centered design Design approach that focuses on human needs and experiences	MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 730 – FIELDWORK
Data Science Project Design: Research Methods
Hypothesis development Formulating testable predictions based on observations	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 670 – DATA VISUALIZATION CMPS 531 – DATA STRUCTURES AND ALGORITHMS DATA 730 – FIELDWORK
Defining data-driven questions Crafting questions that can be answered through data analysis	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 670 – DATA VISUALIZATION DATA 687 – TIME SERIES DATA CMPS 531 – DATA STRUCTURES AND ALGORITHMS DATA 730 – FIELDWORK
Computational logic Application of logical reasoning in computational problem-solving	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Data-driven decision making Making decisions based on data analysis rather than intuition	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 670 – DATA VISUALIZATION DATA 687 – TIME SERIES DATA CMPS 531 – DATA STRUCTURES AND ALGORITHMS DATA 730 – FIELDWORK
Data/research lifecycle Complete process from data collection to research conclusions	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Analysis and presentation of decisions Communicating analytical findings to support decision-making	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Data Science Project Design: Data
Data acquisition Process of gathering data from various sources	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING CMPS 664 – BIG DATA AND DATABASE DESIGN DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Data governance Management of data availability, usability, integrity and security	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 730 – FIELDWORK
Data provenance and citation Documentation of data sources and proper attribution	DATA 620 – ETHICS IN DATA AND COMPUTING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Data Science Project Design: Open Science by Design
Reproducibility, replicability, repeatability Ensuring research can be verified and repeated	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Interactive computing Computing environment that allows real-time user interaction	CMPS 530 – PYTHON FOR DATA SCIENCE
Data Science Project Design: Visualization
Grammar of graphics System for describing and building statistical graphics	DATA 601 – INTRODUCTION TO DATA SCIENCE CMPS 530 – PYTHON FOR DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 670 – DATA VISUALIZATION DATA 730 – FIELDWORK
Static and dynamic visualization design Creating both fixed and interactive data visualizations	DATA 601 – INTRODUCTION TO DATA SCIENCE CMPS 530 – PYTHON FOR DATA SCIENCE MATH 570 – APPLIED STATISTICS MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 670 – DATA VISUALIZATION DATA 730 – FIELDWORK
Data Science In Practice: Responsible Practices
Relevant domain knowledge for effective decision-making Understanding the specific field or industry context	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Legal considerations Understanding legal requirements and constraints in data use	DATA 620 – ETHICS IN DATA AND COMPUTING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Data privacy Protection of personal and sensitive information in datasets	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS MATH 540 – CRYPTOGRAPHY DATA 730 – FIELDWORK
Data and product/system security and resilience Ensuring robust protection against threats	MATH 540 – CRYPTOGRAPHY
Data and product/system governance Oversight and management of data systems	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 730 – FIELDWORK
Research integrity Adherence to ethical principles in research conduct	MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Assessment, monitoring, and management of risks Systematic approach to identifying and controlling risks	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 730 – FIELDWORK
Understanding and uncovering bias Identifying and addressing systematic errors in data and analysis	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING
Interpretability and Explainability Making complex models understandable to humans	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Human impacts of design Considering how design decisions affect people and communities	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS
Responsible data collection Ethical approaches to gathering data from individuals and communities	DATA 620 – ETHICS IN DATA AND COMPUTING
Understanding impacted communities Recognizing how data work affects different groups of people	DATA 620 – ETHICS IN DATA AND COMPUTING MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Data Science In Practice: Effective Collaboration
Working with stakeholders Collaborating effectively with various project participants	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Working with domain experts Partnering with subject matter experts	DATA 601 – INTRODUCTION TO DATA SCIENCE DATA 620 – ETHICS IN DATA AND COMPUTING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Project management Planning, executing, and controlling project activities	DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Infrastructure cost and benefits Evaluating financial and operational aspects of technology infrastructure	DATA 601 – INTRODUCTION TO DATA SCIENCE
Participatory research / stakeholder engagement Including community members in research processes	DATA 730 – FIELDWORK
Data Science In Practice: Communication
Technical writing skills Communicating complex technical information clearly	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Communication (oral) and presentation skills Effectively presenting information to audiences	DATA 601 – INTRODUCTION TO DATA SCIENCE CMPS 664 – BIG DATA AND DATABASE DESIGN MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK
Documentation Creating clear and comprehensive project documentation	DATA 601 – INTRODUCTION TO DATA SCIENCE MATH 680 – ADVANCED MATHEMATICAL MODELING DATA 745/750 – DATA SCIENCE THESIS DATA 730 – FIELDWORK

Categories: Data Science, MSDS

August 15, 2025DMC Lecture Series: The Future Is Not Fixed: How AI & Non‑Deterministic Systems Are Reshaping Work

The first event for this year’s DMC Lecture series features a Ramapo Computer Science alum – Save the date!

Tuesday September 9th, 2025
ASB 429 @ 5:30pm

Artificial intelligence is changing how we work, moving us from predictable processes to systems that operate on probabilities and uncertainty. This talk explores what this shift means for students entering the workforce. Devinder will examine how humans and AI collaborate in practice, what skills are becoming essential, and how to make responsible decisions when working with uncertain technologies. Students will learn practical strategies for thriving in careers where adaptability and critical evaluation of AI outputs are key professional skills.

Speaker Bio
Devinder Sodhi is a Ramapo College Computer Science graduate (2016), who currently shapes AI curriculum development at DataCamp and leads technology communities at Frontier Tower and AICamp SF. His engineering background spans diverse industries, including jet engine testing systems at Qt, surgical robotics at Johnson & Johnson, and medical imaging platforms at Canfield Scientific. Sodhi focuses on practical AI implementation and education, teaching professionals how to effectively integrate AI tools into their workflows while maintaining critical oversight of probabilistic outputs.

Categories: Data Science, Lecture Series, MSCS, MSDS

January 28, 2025DMC Fair keynote speaker: Michael Geraghty

Director of the NJ Cybersecurity and Communications Integration Cell (NJCCIC) and Chief Information Security Officer for the State of New Jersey

DMC Fair Save-The-Date!

Date/Time: Wednesday, 4/23/25, 5-7 pm

Location: Ramapo College, Trustees Pavilion

Join us for the Center for Data, Mathematical, and Computational Sciences (DMC) Fair on April 23th, 2025.

Following the keynote speech, attendees can view posters showcasing Ramapo students’ research projects in Data, Mathematical, and Computational Sciences. Attendees will be able to network with Ramapo students and faculty as well as industry professionals. Awards will be given for the best posters and refreshments will be served.

From Bytes to Insights: Practical Data Science Applications in Cybersecurity

In this presentation, Michael Geraghty, Chief Information Security Officer (CISO) for the State of New Jersey will bridge the gap between academic data science concepts and their real-world applications in the field of cybersecurity. Drawing from extensive industry experience, Mr. Geraghty will demonstrate how data science techniques are applied in threat detection, risk assessment, and incident response in enterprise environments. Through case studies and practical examples, attendees will gain insights into the challenges and opportunities of applying data science in a cybersecurity context. By the end of the session, attendees will have a clearer understanding of how their data science skills can be applied to solve real-world cybersecurity problems, as well as the career paths available in this rapidly evolving field. The talk will conclude with a Q&A session.

Michael Geraghty is the State of New Jersey’s Chief Information Security Officer (CISO) and Director of the New Jersey Cybersecurity and Communications Integration Cell (NJCCIC). In these roles Director Geraghty is responsible for the development and execution of the State’s cybersecurity strategy. He is responsible for leading and coordinating New Jersey’s cybersecurity efforts while building resiliency throughout the State and has direct responsibility for all aspects of statewide cybersecurity operations; governance, risk and compliance; and incident response.

Mr. Geraghty is an accomplished cybersecurity executive with a history of building innovative and model programs in private and public sector enterprises including roles as CISO of the Hudson’s Bay Company, Chief Information Officer of the National and International Centers for Missing and Exploited Children, Vice President of High Technology Investigations at Prudential Financial, and Network Intrusion Detection Manager, Lucent Technologies/Bell Labs. Mr. Geraghty began his career with the New Jersey State Police, where he served 12 years and led the formation and development of its High Technology Crimes Investigations Unit.

He has provided expert testimony before the United States Congress and in federal, state, and international courts on computer crime investigations and forensics. Geraghty is a past president of the Northeast Chapter of the High Technology Crimes Investigation Association and has held leadership roles in the National Strategic Policy Council on Cyber and Electronic Crime.

Categories: Data Science, Lecture Series, Mathematics, MSCS, MSDS

October 22, 2024Larry Shapiro - AI implementation for Finance Operations

AI implementation in Finance operations

Speaker: Larry Shapiro from PwC
When: Monday 11/11 at 6:30pm – Room ASB 136
Topic: AI implementation in Finance operations: Larry Shapiro leads the Global Oracle ERP implementation at PwC including application of AI to finance operations and engagement management. Larry’s talk will resemble a real life case study on how leaders in large global enterprises embrace AI.

Some of the topics he will cover include:

Leadership Concerns:

Risk Management: AI-related risks (e.g., model drift, bias, security breaches).
Regulatory Compliance: Adhering to regulations (e.g., GDPR, CCPA, FINRA).
Return on Investment (ROI): Measuring AI’s financial benefits.

Ethical Considerations:

Bias and Fairness: Ensuring AI systems don’t perpetuate existing biases.
Transparency and Explainability: Understanding AI-driven decisions.
Data Privacy: Protecting sensitive financial information.

Strategic Imperatives:

Digital Transformation: AI’s role in modernizing finance operations.
Competitive Advantage: Leveraging AI for financial insights and agility.
Talent and Skills: Upskilling/reskilling workforce for AI-driven finance.

Implementation Challenges:

Change Management: Overcoming organizational resistance.
Data Quality and Integration: Ensuring accurate and unified data.
Scalability and Security: Deploying AI solutions enterprise-wide.

Please contact Jane Riff ( jriff@ramapo.edu ) for more information.

Categories: Data Science, Lecture Series, Mathematics, MSCS, MSDS

February 20, 2024A personal view of the Design and Evolution of C++ - Bjarne Stroustrup

Bjarne Stroustrup, inventor of the C++ language and a pioneer in the field of Computer Science, is coming to Ramapo College on March 13th at 3:30 pm. Join us to learn about the past, present, and future of the language, and Dr. Stroustrup’s personal insights on its impact on the field of Computer Science . Dr. Stroustrup is currently a Professor of Computer Science at Columbia University in New York, NY.

All are welcome! Food will be available, along with a raffle for Google earbuds – courtesy of the Computer Science Club and co-sponsors. Check back here for room information as we get closer to the event.

Categories: Data Science, Lecture Series, MSCS, MSDS

November 6, 2023DMC Thesis Information Session

The Fall 2023 Thesis Information session was held November 6th, if you missed it – here’s the link to the slides from the presentation. Reach out to Dr. Frees or Dr. Beecher for more information!

For graduate students looking to complete their Thesis in Spring 2024, you should be getting started with your thesis proposal – or at least starting to bounce ideas off potential thesis advisors!

For more information, take a look at the Thesis Handbook and Thesis Archive page as well.

Categories: MSCS, MSDS

March 29, 2023Data in the Music Industry

Tuesday, April 4, 5:00 PM – 6:30 PM – SC-158 (Alumni Lounges)

Please join us for a talk by MSDS Alum Keith Osani, VP of Global Technology Data & Analytics at Sony Music!

This session will discuss the type of data available to music companies from digital platforms such as Spotify, Apple Music, Amazon, YouTube, TikTok and more. We will also explore the increasing volumes of data and the data engineering required to handle it all. Insight will also be given into the tools that data analysts are using to access the data and what types of analysis they are performing.

Categories: Data Science, Lecture Series, MSDS

March 15, 2023Decoding Data Science: A secret recipe for success

Thursday, March 30, 2023 7-8pm – ASB 323

Please join us for a DMC Lecture Series event: Decoding Data Science: A secret recipe for success, given by Dr. Kanad Basu, Data Science Leader & Applied Mathematician!

Abstract of talk:
Data Science has completely transformed all major business sectors such as IT, Banking, Healthcare, Entertainment, Sports & much more. In order to make better & smarter decisions, businesses all over the world have long gathered & analyzed data. The global data science platform market was worth US $ 3.93 billion in 2019 and US $ 4.89 billion in 2020. From 2020 to 2027, it is predicted that the Data Science market will increase at a compound annual rate of 26.9 percent. There has never been a better time to study analytics & data science and contribute to this exciting field.

In this talk Kanad will share his transition journey, lessons picked up along the way, the skills one must develop for being a successful data scientist and how to prepare & market yourself for the job market.

About the author:
Kanad Basu, an interdisciplinary scientist who has 18+ years of analytics and data science experience spanning across academia and industry. Kanad holds the prestigious “Professor of Practice” position at Thunderbird School of Global Management, Arizona State University. He also led a market research data science team at Medallia that focuses on building a consumer behavioral intelligence and benchmarking platform that combines millions of consumer activities to allow businesses to understand where, how, and why consumers spend their time and money. Before Medallia, he led the computer vision data science team at Covisus where he and his team implemented state of the art computer vision algorithms to keep our nation safe by securing supply chains, protecting global compliance for pharmaceutical and medical industries and aiding brands in the prevention of product tampering.

Kanad holds a PhD in Applied & Computational Mathematics, has multiple publications, patents & book chapters around big data analytics and statistical applications in the field of Mathematical Biology & Mathematical Finance. Kanad is a frequent speaker at national & international conferences in AI & Data Science.

Categories: Data Science, Lecture Series, MSCS, MSDS

February 14, 2023Maximum Likelihood Estimation for Discretely Observed Multivariate Vasicek Processes

Tuesday, February 21, 2023 – Via WebEx

Please join us for a DMC Lecture Series event: Maximum Likelihood Estimation for Discretely Observed Multivariate Vasicek Processes, given by Dr. Michael Pokojovy, Assistant Professor, Department of Mathematical Sciences, PhD Program in Data Science, PhD Program in Computational Science, The University of Texas at El Paso (UTEP)!

This talk will discuss analyzing multiple zero-coupon bonds simultaneously to forecast future short rate dynamics. Those forecasts can play important roles in risk management, portfolio optimization, and other applications.

Because of low correlation with other asset classes, bonds play major roles in portfolio diversification efforts. A pure discount or a zero-coupon bond is a contract that does not involve intermediate interest payments but is traded at a deep discount, rendering yields at maturity when redeemed at full face value. As investment funds can create robust diversified portfolios with bonds, it is imperative that multiple bonds be analyzed simultaneously. The classical Vasicek model studies individual zero-coupon bonds and assumes the instantaneous interest rate follows a mean reverting process. In this talk, we consider an extension of the original Vasicek model to multiple zero-coupon bonds. The resulting coupled model is given by a stochastic differential equation driven by a p-dimensional white noise process. Given a set of observations over an equispaced time grid, our goal is to calibrate the system and forecast future short rate dynamics. Those forecasts can play important roles in risk management, portfolio optimization, and other applications. This is joint work with Ebenezer Nkum and Thomas M. Fullerton (UTEP).

Michael Pokojovy is an Assistant Professor of Data Science and Statistics. He holds a Ph.D. and a Dipl.-Math. degree in Mathematical Sciences, both from the University of Konstanz, Germany. Prior to his current appointment at UTEP, Dr. Pokojovy has held several postdoctoral positions in Europe and the US. His research interests include Statistical & Machine Learning, Big Data Analytics, Scientific Computing, etc. In addition to numerous theoretical and methodological developments, he has a track record of collaborative research in statistical process control, quantitative finance, engineering, biomedical sciences, rational mechanics, etc. He has authored and co-authored a number of publications in a variety of professional journals. Dr. Pokojovy has also mentored and advised numerous undergraduate, graduate and doctoral students as well as postdoctoral scholars who are now successfully pursuing various exciting careers in industry or academia.

This talk was supported by a grant from the Ramapo College Foundation.

WebEx information will be provided upon registration

Categories: Data Science, Lecture Series, MSCS, MSDS

January 3, 2023Data Storytelling: The Secret to Delivering Business Impact with Analytics

Thursday, January 26, 2023 – 7-8pm – Location ASB 323

Please join us for a DMC Lecture Series event on Data Storytelling: The Secret to Delivering Business Impact with Analytics, given by Ganes Kesari, the Co-founder and Chief Decision Scientist at Gramener!

Did you know that over 50% of analytics projects fail due to bad data storytelling? Organizations invest heavily in hiring data science experts, buying expensive licenses, and setting up analytics processes. However, without the right data visualization, all this effort will go to waste. Stories have the power to engage people and inspire them to action. This session will introduce you to the concept of data storytelling. It will reveal the 4 steps to mastering visual storytelling with data using exciting industry examples.

Takeaways:

Understand the top challenges in converting data into actionable business decisions
Learn the 4-steps to building powerful data stories using real-world case studies
Find out how data visualization can drive decision-making at organizations

Ganes Kesari is the Co-founder and Chief Decision Scientist at Gramener , a data science company that helps organizations present data insights as stories. He advises executives on data-driven leadership and helps organizations adopt a data culture. Ganes is a TEDx speaker and Forbes Contributor. Find his latest work here: gkesari.com.

This talk was supported by a grant from the Ramapo College Foundation.

Categories: Data Science, Lecture Series, MSCS, MSDS

Center for Data, Mathematical, and Computational Sciences

August 29, 2025Learn how Ramapo's MSDS aligns with the nationally recognized ADSA core Data Science competencies

Data Collection Design

Inference

Modeling (Stochastic)

Multivariate Analysis

Statistical Learning

Bayesian Methods

Causal inference

Model uncertainty

Experimental design

Sampling

Set theory and basic logic

Matrices and basic linear algebra

Optimization – algorithm

Probability theory

Arithmetic and Geometry

Graph Theory and Networks

Exploratory Analysis

Variable Distributions

Scatter Plots

Correlation Analysis

Conditional Probability

Spatial Analysis

Data Visualization

Artificial Intelligence

Classical AI

Modern AI/Data Driven AI

Machine Learning

Classical ML

Deep Learning

NLP

Uncertainty Quantification/Characterization

Data Mining

Model Development and Deployment

Model Risks and Mitigation Strategies

Model analysis and Validation

Data Visualization

Data Structures

Algorithms

Simulations

Data Engineering

Database Design

Data Preparation and Cleaning

Records Retention and Curation

Big Data Systems

Data Security and Privacy

Cloud Computing

High Performance Computing

Programming

Collaboration and version control

Database/data warehousing

Implications of analysis and results

Defining the user and UX design

Story-telling with data

Human-centered design

Hypothesis development

Defining data-driven questions

Computational logic

Data-driven decision making

Data/research lifecycle

Analysis and presentation of decisions

Data acquisition

Data governance

Data provenance and citation

Reproducibility, replicability, repeatability

Interactive computing

Grammar of graphics

Static and dynamic visualization design

Relevant domain knowledge for effective decision-making

Legal considerations

Data privacy

Data and product/system security and resilience

Data and product/system governance

Research integrity

Assessment, monitoring, and management of risks

Understanding and uncovering bias

Interpretability and Explainability

Human impacts of design

Responsible data collection

August 15, 2025DMC Lecture Series: The Future Is Not Fixed: How AI & Non‑Deterministic Systems Are Reshaping Work