Mid-Term Project

Nomis Solutions B

Project Overview

The mid-term project is a group assignment that requires you to apply data visualization methodologies to solve a real business problem. You will work with the case study “Nomis Solutions B” to demonstrate your understanding of data visualization and analytical techniques.

WarningCase questions

You are required to address the following questions:

  • What is the right price to quote?
  • Has e-Car been mispricing its APR quotes? How do you know?
  • How should we start analyzing the data that e-Ca has provided?
  • Which variable should we manipulate>
  • Do you see any particular challenges in evaluating the data?
ImportantKey Information
  • Group Size: 3-4 students per group
  • Due Date: November 11, 2025
  • Presentation: November 13, 2025
  • Weight: 50% of final grade

Deliverables

  1. Written Report (3,000 words maximum)
  • 10 plots (max)
  • Executive summary
  • Methodology
  • Results & analysis
  • Your recommendations to address the case problem
  1. Reproducible Code
  • Well-documented computer code1
  • Clear data preprocessing steps
  • Commented analysis procedures
  • Code that generates all figures and tables in the report

Case Materials

Submission (November 11, 15:59 PM)

Upload a single compressed folder (.zip or .tar.gz) containing:

  1. Report (PDF format, max 3,000 words)
  2. Computer code with processed datasets
  3. README.md with instructions to reproduce results

Presentation Preparation (November 13)

  • Bring presentation on USB drive or have it accessible online
  • Prepare for 10-minute presentation + 5-minute Q&A
  • All team members must participate in the presentation
  • Be ready to answer technical and business questions
  • Presentation schedule available in due course

Getting Help

Office Hours

  • When: Wednesdays 15:00 - 17:00, or by appointment
  • Where: Faculty office or online (schedule via email)
  • Purpose: Methodology guidance, technical support, feedback on progress

Resources

  • Course materials: Slides, readings, and case discussions
  • Peer collaboration: Discuss concepts (but not share solutions) with other teams

Academic Integrity

WarningImportant Reminders
  • Collaboration within teams is expected and encouraged
  • Collaboration between teams should be limited to conceptual discussions
  • All sources must be properly cited using academic citation standards
  • Code originality: While you may reference online resources, the analysis must be your own work
  • Data integrity: Do not modify the provided dataset beyond standard preprocessing

Success Tips

TipRecommendations for Success
  1. Start early: Data visualization can present substantial uncertainty
  2. Plan your approach: Discuss methodology before diving into coding
  3. Iterate frequently: Regular team check-ins and progress reviews
  4. Focus on business value: Always connect technical findings to business implications
  5. Document everything: Keep detailed notes of your analytical choices and assumptions
  6. Practice your presentation: Rehearse timing and smooth transitions between speakers

Good luck with your mid-term project! This is an excellent opportunity to apply data visualization to real-world challenges and develop skills highly valued in today’s data-driven business environment.

Footnotes

  1. Admitted programming languages are C, C++, Julia, Python, R, Rust.↩︎

  2. Data in Excel format. You can red that using R’s readxl or Pandas’s read_excel↩︎