Center for Data, Mathematical, and Computational Sciences

September 11, 2025Save the date: Pivoting C.S. Degree into a Career into Forensics and Cyber Security

Join us for the second DMC Lecture Series of Fall 2025 – and the second by a Ramapo CS Alum!

Pivoting a C.S. Degree into a Career in Forensics and Cyber Security

November 12th, 5:30pm
ASB 524

A degree in Computer Science provides the foundation for solving real-world problems in cybersecurity and digital forensics. In this presentation, I will share how core programming and problem-solving skills translated into developing custom forensic scripts, building in-house automation tools, and streamlining complex data processes. I will also highlight how computer science fundamentals extend into my everyday work dealing with developers to senior leadership.

Craig Brady is a Senior Director of Cybersecurity with over 17 years of experience leading high-performing teams across incident response, threat intelligence, engineering, and vulnerability management. Craig specializes in building scalable security operations programs, developing insider threat frameworks, and guiding organizations through complex mergers, compliance requirements, and emerging threat landscapes. Beyond his technical expertise, Craig is recognized as a collaborative leader and mentor, focused on professional growth, team development, and fostering a culture of accountability. Outside of cybersecurity, he enjoys 3D printing, smart home projects, and traveling.

Categories: Lecture Series, MSCS, MSDS

September 9, 2025Highlight Your Internship Story: Emily Morra - New Jersey Jackals

Welcome to the DMC’s Highlight Your Internship Story series, where we showcase the impressive work our students are doing during their internships. These stories highlight how hands-on experience is helping them grow professionally and launch successful careers in tech.

Congratulations to Emily Morra, Data Science major at Ramapo! This summer, Emily worked with the New Jersey Jackals, a semi-professional baseball team, as an Analytics Intern. She manned the TrackMan Baseball system, which uses AI cameras around the stadium to track pitches and generate information on a wide variety of useful statistics, such as spin rate, exit velocity, launch angle, and more. The analytics team uses this data to create scouting reports for coaching staff and players ahead of each series or trade transaction. Being able to translate the raw data into understandable reports was a skill that could be translated into any concentration of data science, and the fast-paced environment tested her quick learning and thinking skills. Emily has a Sports Management minor and is looking to pursue a career in Sports Analytics, and this experience served as a meaningful step towards her future.

If you are in any of the DMC majors – Computer Science, Data Science, Cybersecurity, Mathematics, and Bioinformatics – and would like to be featured, please contact Dr. Al-Juboori (aaljuboo@ramapo.edu) or Dr. Frees (sfrees@ramapo.edu)!

Categories: Data Science, Internship Story

September 8, 2025Save the Date - Internship Experience Panel Talk

We’re excited to announce that our upcoming event will take place on Tuesday, October 14, 2025, from 6:00–7:00 pm. This session will feature a student panel highlighting their internship experiences—covering everything from the projects they worked on and skills they gained to the challenges they overcame and the advice they have for peers. More details will be shared soon, but for now, mark your calendars and get ready for an engaging and insightful evening.

This event is co-sponsored by the Computer Science Club, Cahill Center and the DMC Lecture Series.

Please check back for more information about location and event registration.

Categories: Internship Story, Lecture Series

August 30, 2025Ramapo College Awarded Prestigious NSF S-STEM Grant to Support Next Generation of STEM Leaders

Ramapo College of New Jersey has received a $2M U.S. National Science Foundation S-STEM (Scholarships in Science, Technology, Engineering, and Mathematics) grant to launch the Ramapo Opportunities for Advanced Degrees in STEM (ROADS) program. This multi-year award will provide comprehensive scholarship support and specialized academic programming for academically talented students with financial need.

ROADS targets five high-growth disciplines through our Center for Data, Mathematical, and Computational Sciences: Computer Science, Data Science, Cybersecurity, Mathematics, and Bioinformatics. These fields are experiencing unprecedented demand, with job growth projections ranging from 11% to 36% over the next decade – far exceeding the national average.

What makes this program particularly innovative is its integration with Ramapo’s accelerated 4+1 degree pathways. Students can complete both their bachelor’s and master’s degrees in just five years, with significant cost savings. During their senior year, students take three graduate courses covered by undergraduate tuition, saving over $12,000 toward their graduate degree.

The scholarship program addresses both financial and developmental barriers that often prevent talented students from succeeding in STEM fields. Selected students will receive full coverage of their unmet financial need – up to $15,000 annually for undergraduates and $20,000 for graduate students. Beyond financial support, ROADS scholars will participate in a unique four-course research sequence designed to develop their identity as researchers and prepare them for advanced careers or graduate study.

The program’s comprehensive support system includes cohort-based mentorship, one-on-one faculty advising, undergraduate research opportunities, and professional development activities. Students will have access to conference presentations, career fairs, and networking events with industry professionals through our established advisory board.

This award builds on Ramapo’s strong tradition of supporting STEM, including our Upward Bound-Math Science Program and Educational Opportunity Fund initiatives. The ROADS program specifically serves the New York-New Jersey region, which ranks as the number one area nationally for job postings in data and computer science fields.

Applications for the first cohort of ROADS recipients will open soon, with separate application cycles for incoming freshmen and current students. The program aims to support 35 students over five years, with the goal of achieving 95% retention rates and ensuring 90% of graduates enter ROADS-related careers within two years.

This investment in STEM education directly supports national priorities in innovation and economic competitiveness, preparing graduates to contribute to cybersecurity, data analysis, and technological advancement in an increasingly digital world.

More information about application processes and program requirements are available NOW.

Categories: Uncategorized

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.

Request Information Apply Now

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 26, 2025Thesis Information - Fall 2025 and Spring 2026

If you’re aiming to graduate in Spring 2026, you must register for Thesis Proposal (DATA/CMPS/MATH 745) this Fall semester. This one-credit, pass/fail course guides you through crafting and securing approval of your formal thesis proposal; upon passing, it enables you to enroll in Thesis (750) the following term.

The Thesis (750) is a two-credit, independent research project requiring a written deliverable, presentation, and faculty panel evaluation.

Who should attend the info session?

Any student planning to graduate in Spring 2026 and needing to complete the 745/750 sequence should join. Come right after the DMC Lecture Series talk on September 9th, and join us in ASB 123 around 7:15pm for all the essential details: what to expect from 745, how to prepare, timeline checkpoints, and next steps.

Categories: Uncategorized

August 26, 2025Join the ICPC Programming Competition

The ICPC (International Collegiate Programming Contest) Regional Competition will take place during the Fall 2025 semester (likely at the end of October or mid-November). This is a great opportunity to enhance your programming and problem-solving skills while collaborating with students from other schools in New Jersey, New York, and Connecticut.

Each team will consist of three students, who will work together to solve 10 challenging programming problems within 5 hours. Training will be provided during the semester and in preparation for the contest.

If you are interested in joining a team, contact Dr. Ali Juboori – aaljuboo@ramapo.edu. Once your participation is confirmed, additional resources and guidance will be shared to help you prepare.

Learn more about ICPC

ICPC Global

ACM GNYR

GNY ICPC Regional

This is an excellent way to strengthen your programming abilities, critical thinking, and teamwork skills!

Categories: Uncategorized

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

July 30, 2025Highlight Your Internship Story: Prashant Shah - Trimble Inc

Congratulations to Prashant Shah, Computer Science / Data Science double major at Ramapo! Prashant’s summer as a Software Engineering intern at Trimble Inc. was an invaluable experience. The theoretical concepts from database design principles to software architecture patterns he learned at Ramapo became the foundation for solving actual business challenges. Working with C# .NET framework and Blazor WebAssembly, he developed configuration interfaces that users depend on, bridging the gap between academic learning and industry demands.

The fast-paced environment taught Prashant skills beyond coding. He mastered Agile development practices, learned to navigate complex enterprise systems, and discovered how to balance technical constraints with user needs. Database optimization techniques from his coursework proved essential when handling performance challenges, while his data science background helped him understand the broader implications of the systems we were building. Perhaps most valuable was learning the collaborative nature of software development. Dev meetings revealed how feedback shapes features, while working with APIs and distributed services showed Prashant the interconnected reality of modern applications. The introduction of AI-powered development tools like Cursor and Copilot opened his eyes to how the industry is evolving, demonstrating that staying current with emerging technologies is crucial.

By the final showcase, Prashant had contributed to a production-ready application, experienced the complete software development lifecycle from conception to deployment, and gained confidence in his ability to deliver meaningful solutions in professional settings. This internship reinforced his passion for technology while providing practical skills that will serve his throughout his career.

Categories: Internship Story

July 27, 2025Highlight Your Internship Story: Edy Martinez - Bristol Myers Squibb

Congratulations to Edy Martinez, Cybersecurity major at Ramapo! This summer, Edy is interning at Bristol Myers Squibb as an IT Systems Intern, working at the intersection of AI and pharmaceutical research. He’s been involved in AI-driven data extraction projects and the management of asset and oncology research data. This experience has given him valuable insight into the corporate world and taught him what it means to work in an environment where patient safety is the top priority. Edy has especially enjoyed connecting with colleagues from diverse backgrounds, and the vanilla lattes from the office’s coffee machine!

Categories: Internship Story

Center for Data, Mathematical, and Computational Sciences

September 11, 2025Save the date: Pivoting C.S. Degree into a Career into Forensics and Cyber Security

Pivoting a C.S. Degree into a Career in Forensics and Cyber Security

September 9, 2025Highlight Your Internship Story: Emily Morra - New Jersey Jackals

September 8, 2025Save the Date - Internship Experience Panel Talk

August 30, 2025Ramapo College Awarded Prestigious NSF S-STEM Grant to Support Next Generation of STEM Leaders

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

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