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Capstones

Capstone projects at Vanderbilt Data Science serve as the backbone of our students’ academic journey, offering them the chance to apply their skills in real-world settings throughout the degree. By partnering with industry leaders, we create opportunities for students to tackle complex, practical challenges. These collaborations not only bridge the gap between classroom learning and professional application but also help shape the future of data science, one project at a time.

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What is a Capstone Project?

The Capstone Project anchors your Data Science journey at Vanderbilt in a real project setting. It provides an opportunity to apply advanced skills, addressing challenges presented by industry partners. This experience bridges the gap between academic learning and professional application, allowing participants to solve complex data problems and deliver impactful results. Finally, you'll practice your communication skills by delivering your results to industry stakeholders.

Capstone Structure and Timeline

Overview:  A Capstone Project follows a structured timeline, ensuring participants can fully apply their data science skills while meeting the expectations of industry partners. The process spans the whole academic year and includes key phases that guide participants from project selection to final deliverables.

Key Phases

  • Project Assignment

    You will be assigned a capstone project and team partners in the weeks before you even begin your first class. The team will include a few classmates as well as your industry lead.

  • Project Scoping and Definition

    Mod 1.
    At the beginning, you'll be doing the "work before the work." Identifying data sources, clarifying project scope and problem statement, defining success metrics and ensuring data availability. This is not technical work, but it is vital to project success.

  • Review Ethical Implications and Data Privacy

    Mod 2. 
    This is more "work before the work" built in at the beginning. What are the ethical implications of your project? Is there proprietary or sensitive data? What are your responsibilities to anyone impacted by the data collection and use? Also, continue any work from Mod 1 not yet finished.

  • The Work

    Mod 3.
    You work with your project team to deliver. Using Github, AGILE and good teamwork practices, you do the technical work needed to solve the problem you've defined and scoped out already. Watch out! Sometimes you need to make a mid-course correction. Team leads, faculty and technical advisors are there to help.

  • Communicate Your Results

    Mod 4. 
    This is the "work after the work." Just because you have a brilliant solution doesn't mean you are done. If nobody knows about it, it is useless. How do you effectively communicate how you solved the problem to stakeholders? How do you adapt business processes so that your solution actually gets incorporated into users workflows? Of course some of the Work from Mod 3 may seep into this phase, but by the end, you'll be presenting.

Where do Capstone projects come from?

Capstone Projects can arise from many different sources. Our primary goal is for you to work on something that is energizing and exciting, such that you look forward to the work and want to talk about it with others. 

  1. Industry partners. We have a group of industry partners that we work with every year to do Capstone Projects. This is where they come from most commonly. A partner will have an idea of a project already and need a team of students to make it a reality.
  2. Faculty partners. Some projects will be with faculty members who are doing cutting edge research.
  3. Bring your own. If you have a job or already have a relationship with a firm, it is possible for you to bring your own Capstone Project. This is rare, but possible. Please contact us early (early June - mid July) before you start if this is something you'd like to pursue.

Roles and Responsibilities

Successful Capstone Projects rely on collaboration between students, faculty advisors, and industry partners. Each plays a critical role in ensuring the project meets its objectives and delivers meaningful results.

  • Students
    • Project Execution: Students are responsible for conducting the research, analysis, and development work required by the project. This includes adhering to the project plan, meeting deadlines, and producing high-quality deliverables.
    • Communication: Regular updates are key to keeping the project on track. Students are expected to maintain clear and professional communication with both their faculty advisor and industry partner.
    • Problem-Solving: Students should proactively address challenges that arise during the project, seeking guidance from their advisors when necessary and iterating on their work based on feedback.
  • Faculty Advisors
    • Guidance and Support: Faculty advisors provide mentorship throughout the project, helping students refine their research questions, methodologies, and overall approach.
    • Feedback: Advisors review the work at key milestones, offering constructive feedback to ensure the project stays on course and meets academic standards.
    • Assessment: At the end of the project, faculty advisors evaluate the student's performance, contributing to the final grade.
  • Industry Partners
    • Project Proposal: Industry partners propose relevant and impactful projects that align with their business needs and provide valuable learning experiences for students.
    • Data and Resources: Partners provide access to necessary data, tools, and other resources, ensuring students have what they need to succeed.
    • Regular Check-Ins: Industry partners engage in regular check-ins with students, providing feedback and direction to keep the project aligned with their expectations.
    • Final Evaluation: Industry partners contribute to the assessment of the project's success, offering insights on the practical value of the work completed.

Expected Outcomes

Completing a Capstone Project at Vanderbilt Data Science is a transformative experience, providing participants with the opportunity to apply their skills and knowledge in a real-world setting. Here’s what you can expect to achieve:

1. Deliverables:

  • GitHub Repository: A well-organized repository containing all code, data, and documentation related to the project. This ensures your work is reproducible and can be easily shared with others in the field.
  • Presentation: A professional presentation that highlights the key aspects of your project, including identifying a business-oriented problem statement, not just tech for the sake of tech, challenges faced, solutions developed, and potential future directions. This is an opportunity to showcase your ability to communicate complex ideas clearly and effectively.

2. Skills Development:

  • Technical Proficiency: Gain hands-on experience with the latest tools and technologies in data science, from machine learning frameworks to data visualization platforms. This practical knowledge is crucial for advancing your career.
  • Project Management: Develop your ability to manage time, resources, and collaboration in a project setting. You’ll learn how to set realistic goals, meet deadlines, and adapt to challenges as they arise.
  • Communication Skills: Enhance your ability to communicate complex data science concepts to both technical and non-technical audiences. This is an essential skill for any data scientist working in a multidisciplinary environment.

3. Professional Growth:

  • Industry Connections: Build relationships with industry partners who may become future employers or collaborators. These connections are invaluable for expanding your professional network and exploring career opportunities.
  • Portfolio Piece: Your capstone project will become a centerpiece of your professional portfolio, demonstrating your ability to apply data science to real-world problems. This can be a significant asset when applying for jobs or further academic pursuits.
  • Personal Achievement: Completing a Capstone Project is a significant milestone in your data science journey, providing a sense of accomplishment and confidence in your abilities.

Capstone Case Studies

Nashville SC: Fan Sentiment Analysis

Students analyzed Reddit discussions to gauge fan sentiment for Nashville SC compared to other local teams. Using AI-driven tools, they identified key insights that helped NSC enhance fan engagement strategies.

AllianceBernstein: ESG Sentiment Analysis

Students collaborated with AllianceBernstein to analyze the impact of ESG-related news on stock prices. Using advanced NLP models, they identified how negative ESG sentiment could predict potential declines in stock returns, providing valuable insights for sustainable investment strategies.

HCA: AI for Medical Diagnostics

In collaboration with HCA Healthcare, a student fine-tuned an AI model to improve the identification of vascular conditions from radiology reports. By enhancing the accuracy of medical diagnoses, this project aims to streamline healthcare processes and improve patient outcomes.

Preverity: Predicting Pediatric Malpractice Risks

A predictive model was developed to assess malpractice risks in pediatric healthcare. By leveraging logistic regression with Lasso regularization, the project aimed to enhance patient safety by identifying potential risks in medical practices.

Explore All Our Case Studies