The UVA School of Data Science, in partnership with Riipen, is launching a co-curricular, project-based learning program for students in the undergraduate Data Science program. This initiative provides US-based employers with the opportunity to engage highly motivated students who are eager to apply their analytical and problem-solving skills to real-world challenges, while providing employers with fresh insights, innovative solutions, and a pipeline of emerging talent. 

About the Learners 

Learners in this program are in their second year of college and have just completed their first semester of dedicated data science coursework in the UVA School of Data Science B.S. program. By the time they reach the project stage, they will have: 

• Foundational Knowledge – Coursework in statistics and probability applied to introductory data problems. 
• Programming Skills – Hands-on experience with Python, R, and SQLite for data wrangling, cleaning, and visualization. 
• Data Preparation – Ability to clean, transform, and structure datasets, including handling missing values and assessing quality. 
• Exploratory Data Analysis – Competence in EDA to identify trends, patterns, and outliers. 
• Visualization & Communication – Ability to create basic dashboards and visual summaries, and present results clearly. 

These learners bring professionalism, curiosity, and a solutions-oriented mindset. While still early in their training, they are capable of delivering beginner-level data science projects such as data cleaning, exploratory analysis, and basic modelling, within the scope of a short, 2-week project.

Project Details 

• Entry-level scope, designed to match beginner project complexity. 
• Runs January 2–16, 2026: students dedicate one week full-time to core project work, followed by one week of lighter effort for polishing and final deliverables. 
• Employers should expect to attend a final presentation between January 16th-23rd
• Employers should expect that student groups may want to meet in December to discuss the project, but the official kick-off will be January 2nd
• Approximately 50 total hours per learner over the 2-week period. 
• Teams of 3 students will be pre-assembled by the program to ensure balanced skills and collaboration. 
• Employers may submit projects for consideration; student teams will rank preferences, and 5 projects will be selected from an anticipated pool of 7 proposals. 
• Structured to build practical skills, strengthen workplace readiness, and deliver tangible value to employers.

Employer Role 

Employer commitment is minimal; typically limited to providing a project brief, dataset (with data dictionary), and a few light check-ins with the student team. Time commitment is ~5-6 hours, with the heaviest interaction during the full-time project week (Week 1) and lighter touch points in Week 2 for polishing and approval. Employer partners should be to attend the student presentation of work sometime between January 16th-23rd, 2026.

What Employers Provide 

• Data & Documentation - A complete dataset with a data dictionary (document outlining the data structure, content, and variable definitions through metadata such as names, types, sizes, classifications, and relationships), delivered before project selection so students can make informed choices (template found here)
• Project Brief – Clear problem statement, objectives, success metrics, constraints, and available tools/access. 
• Point of Contact – One designated POC with a 24-hour response time during Week 1. 
• Access & Compliance – Any required tools, credentials (read-only where possible), and NDA/data-sharing terms. 
• Feedback Windows – Quick turnaround on questions and interim feedback in Week 1; Deliverable feedback in Week 2; active engagement and participation in presentation Q&A

Employer partners engaging with these student teams will benefit from a low-lift way to turn your data into decision-ready insight. You’ll receive professional, ready-to-share outputs (plus reproducible artifacts your analysts can extend) without pulling your team off day-to-day priorities. Along the way, light, timely check-ins keep the work aligned, and you gain early visibility into a pipeline of emerging talent.

Example deliverables might include:

• Data Description: Overview of dataset variables and formats. 
• Cleaning & Preparation: Actions taken to make the dataset usable. 
• Preprocessing Notes: Record of transformations and assumptions. 
• Exploratory Analysis: Basic charts, tables, and statistical summaries. 
• Predictive Model (Optional): Simple classifier or regression with metrics (accuracy, precision/recall). 
• Segmentation/Clustering: Grouping customers, products, or behaviors. 
• KPI/Time-Series Trends: Key indicators tracked over time. 
• Key Findings: Summary of drivers, clusters, inefficiencies, or high-usage patterns. 
• Visualizations: Graphs, dashboards, or tables. 
• Trends & Anomalies: Identification of unusual spikes, lags, or deviations.