January 31 2025 at 4:20 PM | NJ Pathways

NJ Pathways Happening: Streamlining Data Science Education for New Jersey Students

The New Jersey Big Data Alliance (NJBDA) and NJ Pathways are advancing the Data Science Curriculum Alignment and Articulation Agreement pathways project, designed to enhance credit transfers between New Jersey’s community colleges and four-year institutions. This initiative addresses critical challenges like credit retention, cost and time delays, and career uncertainty faced by students pursuing data science degrees. As this research project moves towards its next phase, the current project team gave a virtual Handoff Presentation on December 13, 2024, to present their progress and review next steps. 

Led by Students, For Students
This innovative project is being spearheaded by a team of talented students from universities across New Jersey (some having transferred from NJ Community Colleges to complete their degrees): 

  • Jessica Rippman (Team Lead): B.A. Mathematics (Statistics), Rowan University 
  • Simra Ahmed: B.S. Computer Science, Ramapo College 
  • Rebecca Feit: B.S. Computer Science, Ramapo College 
  • Pranjal Karanjkar: B.S. Business Analytics, Rutgers University 
  • Aliaksandra Voitsik: B.S. Mathematics (Data Analytics), Kean University 

Guided by experienced mentors including Dr. Karen Bemis and Lori Dars, of Rutgers University, and George Avirapattu, of Kean University, the team has conducted in-depth research to identify and resolve gaps in data science curricula. 

Project Highlights: 

  • Key Objective: Standardizing core data science courses to create clear educational benchmarks and minimize redundancies. 
  • Research Approach: The team analyzed syllabi from New Jersey institutions and “gold-standard” schools like UC Berkeley, Carnegie Mellon, and Rutgers to identify overlaps and gaps in learning objectives. 
  • Proposed Curriculum: Six foundational courses recommended: 
    • Introduction to Programming in Python 
    • Data Structures 
    • Statistics/Probability for Data Science 
    • Introduction to Data Science 
    • Ethics for Data Science/AI 
    • Data Visualization 

Next Steps: The team will collaborate with community colleges to refine curricula, incorporate faculty feedback, and propose elective pathways for diverse student needs. 

This initiative aims to empower students by aligning educational pathways with industry demands and ensuring a smoother transition between institutions. 

For more information, review the attached Handoff PPT and visit NJBDA or NJ Pathways.