Data Science Practicum Pathway: DSBA 6390

The Data Science Practicum pathway is an intensive, hands-on, project-based capstone course designed for students who are ready to apply their technical, analytical, and professional skills to real-world business and organizational challenges. 

In today’s organizations, data science deliverables are part of larger technical solutions. Models and visualization must often be connected to data pipelines, APIs, cloud platforms, AI components, testing processes, documentation, and user-facing applications. Therefore, this practicum requires students to apply not only data science skills, but also foundational software engineering practices such as GitHub, branching, complex integration, testing, and technical documentation.

Students work in small teams on instructor-approved projects with external industry sponsors or applied business domains. Students will be asked to submit their resumes, detailed skill inventories, and project preferences; however, final project and team assignments are made by the instructor based on student skills, project needs, sponsor requirements, and team balance.

Projects emphasize core data science competencies, including data wrangling, predictive modeling, machine learning, natural language processing (NLP), cloud/data platforms, software development, RAG, LLM integration, data visualization, integrated testing, collaboration, and communication of actionable business insights using data storytelling concepts.

The practicum is designed to provide a professional project environment. Students are evaluated not only on what they individually develop and deliver, but also on how they work with the team, integrate their work into a larger solution, communicate with stakeholders, respond to feedback, and contribute to the overall project outcome.

Success in this pathway requires more than technical mastery; it demands professional-grade collaboration, proactive communication, and the ability to integrate individual work into a cohesive team solution that meets the business needs.

Practicum Requirements Summary

Students must meet all prerequisite coursework to be ready for this course and secure instructor approval before enrollment.

  • Complete a minimum of 18 credit hours before registration.
  • Demonstrate proficiency in Python, GitHub, Git, Predictive Modeling, Machine Learning, Cloud Computing, Data Engineering and Data Visualization.
  • Have completed the following courses (or equivalents): DSBA 6160, DSBA 6156, and DSBA 6211.
  • Participate actively in team-based project selection, scoping, testing, implementation, documentation and final delivery.
  • Deliver a comprehensive final business presentation and written project report, technical artifacts demonstrating actionable insights, technical rigor, and professional quality.

The Data Science Practicum Pathway: Three Phases

Phase 1: Readiness Assessment 

During the first week, students must demonstrate readiness to participate in a sponsor-based applied project and prepare for successful practicum placement and project scoping:

  • Confirm Eligibility: Ensure all prerequisites and credit-hour requirements are met.
  • Resume: Submit your resume and skill inventory.
  • Team-based Readiness Project: Students will work in assigned groups to complete a rapid data science sprint. This allows the instructor to evaluate technical proficiency alongside team dynamics, reliability, and professional communication. 
  • Project Authorization: The instructor will use this checkpoint to assess whether each student has the technical and professional readiness required for the practicum. 
  • Project Team Formation: Students will be grouped based on interests, technical skills, and project complexity.

Final approval to remain on the course is at the discretion of the instructor. Students who do not demonstrate sufficient readiness may not receive instructor approval to continue in the practicum and may be required to defer enrollment until they are better prepared.

Lack of familiarity with a specific tool or business domain is not automatically disqualifying. However, students must demonstrate the ability to learn quickly, ask questions, use available resources, communicate clearly, and deliver work at the level expected in a final graduate capstone.

Phase 2: Practicum Implementation Phase 

Once enrolled and approved to continue in the practicum, students begin structured project execution with ongoing guidance from the instructor and direction from the sponsor:

  • Develop a written Statement of Work / Practicum Work with the sponsor and instructor. This document will guide the project and define the business objective, key functionality, deliverables, milestones, due dates, assumptions, dependencies, testing expectations, and acceptance criteria. 
  • The Statement of Work/ Practicum Work must be reviewed and signed or acknowledged by the student team, sponsor, and instructor.
  • Progress Reports: Submit periodic project updates and reflect on milestones achieved, challenges faced, and next steps.
  • Instructor Mentorship: Receive technical and strategic guidance during regular check-ins to ensure steady progress, sponsor-ready and professional-quality outcomes.

Phase 3: Final Deliverables & Presentation Phase

In the concluding phase, students synthesize their project results and present outcomes to academic and professional audiences:

  • Business-Ready Deliverables: A project is only considered “done” when the solution is fully integrated with the sponsor’s business and technical requirements, documented for handover, and translated into actionable business functionality.
  • Develop Insights & Recommendations: Translate analytical results into actionable business insights and recommendations.
  • Peer, Instructor Evaluation: Projects will be assessed based on business functionality, technical quality, communication clarity, teamwork, and overall impact. Sponsor’s feedback will be part of the Instructor’s evaluation.
  • Reflection Summary: Students will complete a brief reflection discussing lessons learned.

In the Practicum, a project is not complete when the code runs; it is complete when the sponsor can use the results to decide or power an application. Students are expected to deliver “client-ready” work that bridges the gap between data science theory, data engineering and organizational impact.

Practicum Presentation Process

The practicum culminates in a professional presentation to the sponsor and a full documentation handoff.

  • Presentation Format: Each team presents its business problem, methodology, key findings, and recommendations to faculty and invited guests.
  • Q&A Session (15–20 minutes): Faculty and industry reviewers may ask questions regarding data sources, technical methods, and conclusions.
  • Evaluation Criteria: Presentations are evaluated on analytical depth, technical execution, clarity, business outcomes and professional quality of recommendations.
  • Feedback & Revision: Teams receive feedback and may be asked to refine reports or visualizations before final grading.

Semester Availability & Important Notes

  • DSBA 6390 is offered only in the Fall and Spring semesters.
  • Enrollment is by instructor permission only after project approval.
  • Students may not receive practicum credit for work conducted in a paid internship; instead, they should pursue the DSBA 6400 Internship course if applicable.