September 12, 2022Welcome new MS and 4+1 in Data, Mathematical, and Computational Sciences students!

We are excited to welcome students into our new MS in Applied Mathematics and MS in Computer Science programs at Ramapo College! They join our MS in Data Science students as members of our DMC community. All of these new programs are taught by our dedicated full-time faculty. We had a chance to meet them on Thursday, September 8th at the DMC Meet-and-Greet. We are so happy you chose to continue your education with us!

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April 4, 2022Fall 2022 DMC Course Schedule

1st Year Students

Welcome to Ramapo College of New Jersey! Whether you are entering the MS in Data Science, Applied Mathematics, or Computer Science, there are a few things you will want to take care of to get ready for your first semester. Admissions has a guide for admitted students, which outlines the registration process and a number of other things of interest.

Fall 2022 Course Schedule

• CMPS 547 – Foundations of Computer Science

  This course provides a foundational overview of programming language design, including compiled languages as well as higher level scripting languages. The course introduces students to concepts such as grammars, binding, scope, flow control, and data abstraction – through the lense of multiple languages. Students will also gain experience programming across language interfaces. This course serves 3 core purposes as a foundational course for MSCS students: (1) Principles of Programming Languages – this course serves as a general introduction to the theory of computation and programming language design. Topics cover basic theory of computation, and language principles such as context free grammars; binding and scope; static and dynamic semantics; type safety; recursion; concurrency. The theoretical topics covered throughout the course will be demonstrated / mapped to the programming topics languages introduced. (2) The C programming language – students are introduced to the C as a way to prepare them for courses such as Operating Systems and so they understand aspects of low level programming language to support the Computer Architecture course as well. The fundamental topics in C include types, control flow, structures, arrays, pointers, and relationship to hardware via compilation. (3) Python Programming – students will learn how higher level languages differ from C, and how they map to lower level languages. Significant time will be spent covering a variety of language concepts through the lens of Python, culminating with extending Python via C through Cython.

  Monday nights – 6:05pm – 7:20pm

  Required for MS Computer Science first year students.
  Category 2 elective for MS Applied Mathematics students

• MATH 680 – Advanced Mathematical Modeling

  This course requires students to develop, use, and assess models to solve real-world problems using the mathematical modeling process. Models developed in a variety of disciplines, including linear programming, network science, decision theory, machine learning, are studied and used to solve problems in other disciplines.

  Monday nights – 6:05pm – 7:20pm

  Required for MS Data Science second year students.
  Required course for MS Applied Mathematics students (any year, with pre-requisites).
  Category 2 elective for MS Computer Science students (with pre-requisites).

• DATA 601 – Introduction to Data Science

  This course serves as the foundation for all DATA graduate level coursework. It will cover programming, data analysis, data visualization, ethics and security / privacy concerns surrounding data, and other topics students are expected to master in the program. The course will also feature a Seminar component designed to acclimate students to working with Industry Sponsors and to hear first hand from people working in Data Science.

  Tuesday nights – 6:05pm – 7:20pm

  Required for MS Data Science first year students.
  Category 2 elective for MS Applied Mathematics (recommended for first year students).

• MATH 570 – Applied Statistics

  This course gives an introduction to statistical methods used in data science with an emphasis on applications. Topics may include foundations of probability, univariate and multivariate random variables and distributions, special distributions, Central Limit Theorem, one- and two-sample methods, point estimation, interval estimation, hypothesis testing, regression analysis, Bayesian analysis, data analysis and model building.

  Tuesday nights – 8pm – 9:15pm

Required for MS Data Science full time first year students. May be deferred to future semester for part time MSDS students.
Required for MS Applied Mathematics students full time first year students. May be deferred to future semester for part time MSAM students.
Category 2 elective for MS Computer Science students (recommended for first year students).

• CMPS 530 – Python for Data Science

  This course introduces students to fundamental programming concepts and skills utilized by Data Scientists – in particular parallel computing, I/O, and visualization – all through the Python programming language and associated libraries (i.e. numpy, pandas, etc.). The course is suitable for students with a basic knowledge of programming, and prepares students to take more advanced computing courses in databases, big data analytics, machine learning, and other DATA and CMPS electives.

  Thursday nights – 6:05pm – 7:20pm

  Required for MS Data Science full time first year students. May be deferred to future semester for part time MSDS students.
  Category 2 elective for MS Applied Mathematics students (strongly recommended for first year students).
  Category 1 elective for MS Computer Science students (recommended for first year students).

• MATH 562 – Applied Linear Algebra

  This course is a foundational course for the study of Linear Algebraic structures used in a variety of scientific and computational applications, such as data fitting, clustering, feature engineering, image processing, machine learning, optimization, and dynamical systems. In order to achieve this purpose, this course will cover topics in linear algebra including vector and matrix operations, linear transformations, linear independence, norms, decomposition, and least squares.

  Thursday nights – 8pm – 9:15pm

Category 1 elective for MS Data Science students.
Required for MS Applied Mathematics (strongly recommended for first year students).
Category 2 elective for MS Computer Science students (recommended for first year students).

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March 24, 2022DMC Advisory Board Member - Dobri Yordanov

Ramapo College’s Center for Data, Mathematical, and Computational Sciences welcomes Dobri Yordanov to our Advisory Board! Our Advisory Board’s mission is to assist the Center in aligning our curriculum with industry needs and expectations, helping us to promote our curriculum, and guide us in developing our sponsored fieldwork experiences to our students.

Dobri is a Ramapo ’15 graduate with a BS in Computer Science and Mathematics. He started his career with web and flash freelancing even prior to attending and was a part of developing the infrastructure behind most of the ramapo.edu websites. After graduation, he spent 4 years at Google, working on critical infrastructure on Google Maps, Google Assistant and Daydream (virtual / augmented reality). Facebook was the next prototyping home for him, where he led engineering on a music collaboration experience called Collab as a part of NPE, Facebook’s experiences incubator. Most recently, he’s joined Manticore Games as a principal engineer, leading major efforts to build out the next big thing in game development. Dobri is also an adjunct faculty in computer science at Ramapo, most recently running a course on computer graphics in Unreal Engine 5.

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March 1, 2022Center for Data, Mathematical, and Computational Sciences Fair

Date/Time: Tuesday, 4/5/22, 5-7pm
Location: Ramapo College, Trustees Pavilion 1

Join us for the Data, Mathematical, and Computational Sciences Fair on April 5th, 2022! This event will feature keynote speaker Dr. Shawn Simpson, Principal Data Scientist at BlackRock AI Labs, who will share practical tips gleaned from her career as a Data Scientist in finance, media, and advertising. Following the keynote speech, attendees will have the chance to 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 prospective employers who may be looking to hire interns or full-time employees. Awards will be given for the best posters.

This event is open to current and prospective students, faculty, staff, and members of the public.

Register Now

This page will be updated as more information about the event is announced

Schedule

• 5:00-5:05pm Guests arrive
• 5:05pm-5:10pm Welcome remarks (President Cindy Jebb)
• 5:10-5:20pm Welcome and overview of Ramapo’s bachelor and master degree programs in Data, Mathematical, and Computational Sciences (Scott Frees and Amanda Beecher)
• 5:20-6:15pm Keynote speech: “Field notes for future data scientists: tips from a career in industry” by
  Dr. Shawn Simpson, Principal Data Scientist, BlackRock AI Labs. See additional information below.
• 6:15-7:00pm Student poster session and networking event

Keynote Speaker

Speaker: Dr. Shawn Simpson, Principal Data Scientist, BlackRock AI Labs

Title: Field notes for future data scientists: tips from a career in industry

Abstract: When training to be a data scientist there is an emphasis on data analysis and modeling
techniques — but what happens once you are out in industry? This talk will provide practical suggestions based on my career in data science, with examples drawn from applications in news and media, finance, and advertising technology. Topics will include going deep with data, understanding uncertainty, joining forces with engineers, taking a product mindset, knowing your end user, and architecting end-to-end systems.

Speaker bio: Shawn Simpson, Ph.D. is Principal Data Scientist in BlackRock AI Labs, where she leads an initiative that builds AI-powered decision tools for traders. Previously she was Senior Data Scientist at Tapad, a cross-device advertising technology firm. She built large-scale predictive models for telco applications using Scala, Spark/PySpark, Python, Hadoop MapReduce, and Google Cloud Platform technologies, and acted as lead data scientist on cross-functional product teams. Prior to that Shawn was Head of Data Science at Dow Jones. She was responsible for multivariate paywall testing on WSJ.com, predictive models for subscriptions and cancellations, company-wide data science training,
and internal consulting for newsroom, finance, and customer teams.

Before joining industry Shawn was an Assistant Professor of Statistics at Columbia University. Her research focused on analysis of recurrent events, Bayesian methods for large-scale data, and post-
marketing drug safety surveillance. She has a Ph.D. in Statistics from Columbia University and a B.S. in Electrical Engineering from the University of Illinois at Urbana-Champaign.

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February 7, 2022How to fail your tech interview successfully

Feb 22nd at 6-7pm in ASB 332

Computer Science is hard. Being a Software Engineer is hard. Translating your computer science skills into a successful Software Engineer gig after college shouldn’t be hard. After interviewing close to 100 candidates at all levels for Google, Facebook and Manticore, Dobri Yordanov has a few tips and tricks to share, as well as pitfalls to avoid. Join us if you’d like to hear about them, be it out of curiosity or practicality. Everyone is welcome!

Register Now

Biography: Dobri is a Ramapo College ’15 graduate, originally from Bulgaria. Since graduating, he’s built a career as a Software Engineer and a self-described Prototype Wizard in large tech working for Google and Facebook, and most recently landing in the game industry as a principal engineer at Manticore Games. As of ’21, you can occasionally catch him in Ramapo teaching some of our Computer Science classes. He likes long walks on the beach in VR and philosophical ponderings about the nature of our existence and ethics in technology. He will also absolutely listen to you about your favorite algorithm or design paradigm – he is 100% that kind of dork.

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November 3, 2021Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets

Congratulations to Ramapo’s Data Science Professor Osei Tweneboah. Dr. Tweneboah has co-authored the textbook Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets recently published by Wiley. This book is perfect for data practitioners, undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs. It provides a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services.

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November 1, 2021Lecture Series: Data is the New Oil- Why Now?

Wednesday, November 10, 2021 at 5:00 PM until 6:00 PM – Eastern Standard Time

Ramapo College is proud to host a Graduate Lecture Series about a variety of interesting topics throughout the year.

During the RCNJ Lecture Series: Data is the New Oil- Why Now, you’ll have the opportunity to hear from Arnab Mukhopadhyay, Head of Enterprise Architecture at Valley Bank more about exploring a systemic or architecture-based viewpoint on exploring various aspects of data – ranging from an overview on data technologies, data integration, data analytics, data governance, and industry usage of data.

For more information about this particular topic, or questions about the event, please email Professor Scott Frees at sfrees@ramapo.edu and we will be happy to assist you.

You won’t want to miss this exciting and informative Graduate Lecture Series Event.

Register Now

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October 11, 2021Using Machine Learning to Analyze and Predict the Movement of Horseshoe Crabs in Long Island Sound - Recording

We want to thank Dr. Samah Senbel for the wonderful talk on using Machine Learning to analyze the movement of Horseshoe Craps in the Long Island Sounds. In case you missed it, her talk can be viewed here.

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January 21, 2021From Data Ethics to Data Justice: The Challenge of Building Better Worlds through Data Science

Dr. Andrea Pitts, Assistant Professor of Philosophy at the University of North Carolina, Charlotte will be describing this new new branch of study within applied ethics, a subfield in professional ethics that includes areas of research and practice such as computing ethics, media ethics, and biomedical ethics. This new branch of applied ethics responds, specifically, to novel applications and technologies for data storage, maintenance, and processing that add new layers to the study of morally relevant considerations within information ethics, business ethics, and AI ethics, for example.

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August 22, 2020Convolutional Neural Networks for Face Recognition

We are happy to announce that Dr. Ausif Mahmood has rescheduled his talk on Convolutional Neural Networks this fall virtually.

Dr. Mahmood is the Director of The School of Engineering at the University of Bridgeport in Bridgeport, CT. His research areas involve Artificial Intelligence, Computer Vision, Machine Learning, and Deep Learning – bridging ties between Computer Science and Data Science.

Please join us virtually on September 17th at 1:00pm – 2:00pm. Registration is required, registrants will receive web conferencing information a few days prior to the event.

You’ll also have the opportunity to learn more about Ramapo’s new Data Science programs, launching this coming fall.

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