Projects

A.I. - First -- this course prepares students to succeed in today’s AI-first organizations by aligning core projects with real-world business needs and modern AI workflows. Each project immerses students in a different business domain—finance, healthcare, and web-based sales—and culminates in the production of a data- or AI-driven product.

Students don’t just learn about AI and data science—they build, explore, and deliver meaningful outputs that resemble what employers are asking for today. Projects simulate professional roles in analytics, operations, and product strategy, with increasing complexity and autonomy. The final capstone project prepares students to clearly communicate their skills in a job interview, supported by a portfolio-worthy product and story.

Skills: We emphasize practical tool use, teamwork, iterative exploration, and the creation of tangible outputs with business value. 

Projects Summary:

• Project 1: Predictive Modeling for Loan Repayment (Finance)
  Students will create a predictive machine learning model to improve loan decision-making at a peer-to-peer lending company.

• Project 2: Discovering Insights in Health Data (Healthcare)
  Students will use AI and traditional analytics (Exploratory Data Analysis, or EDA) to explore a large health dataset and recommend a valuable new metric to support vaccination outreach.

• Project 3: AI Agent for Internal Knowledge Use (Web Business)
  Students will build an internal-facing AI agent that helps employees access and interact with organizational data using a large language model.

• Project 4: Final Capstone – Create a Valuable Data Product
  Students will work in teams, to create a machine learning model or AI agent that solves a business problem, culminating in a presentation aligned with job interview skills.

Takeaways & Job Preparedness

These projects are designed to be interview-relevant and resume-ready. Students graduate from the course able to say:

• “I’ve built a machine learning model that improved business decisions.”

• “I’ve discovered meaningful insights from a messy, real-world dataset.”

• “I’ve built and deployed a functional AI agent using LLMs for internal business use.”

• “I can present and explain my work through the lens of business impact.”

This course delivers a fast-paced, modern immersion into the world of AI-driven data work—designed for students to be productive in AI-first organizations.

Project 1: Predictive Modeling for Loan Repayment (Finance)

• Business Case: A peer-to-peer lending platform wants to reduce loan defaults.

• Output Modality: Predictive model (Random Forest)

• Students Will Be Able To:

  • Build and evaluate a machine learning model to forecast loan repayment likelihood

  • Interpret model outputs to support lending decisions

  • Use Python and Google Colab to analyze structured datasets

• Dataset: Lending Club loan data

• AI Role: Assistive (code generation, analysis support)

Project 2: Discovering Insights in Health Data (Healthcare)

• Business Case: A public health department seeks new metrics to improve vaccination outreach.

• Output Modality: Exploratory analysis report with visualizations and business recommendations

• Students Will Be Able To:

  • Use AI tools alongside traditional methods to explore a large, complex dataset

  • Identify and explain a data-driven insight or metric of value to a healthcare organization

  • Visualize findings and communicate business value

• Dataset: CDC: National Immunization Survey Data

• AI Role: Assistive and generative (exploration, visualization, synthesis)

Project 3: AI Agent for Internal Knowledge Use (Web Business?)

• Business Case: A web-based [type of] company wants a smarter way to help employees access and interact with internal data and policies.

• Output Modality: Internal-facing AI agent (LLM-powered)

• Students Will Be Able To:

  • Design and prototype an AI agent capable of answering employee questions based on organizational data

  • Load and structure data context to improve AI performance

  • Frame business problems suitable for LLM-based solutions

• Dataset: TBD; proprietary or simulated data related to internal operations

• AI Role: Central (students build an AI agent)

Project 4: Final Capstone – Create a Valuable Data Product

• Business Case: Students choose from three realistic business problems (finance, health, web/sales).

• Output Modality: ML model or AI agent with interview-style presentation

• Students Will Be Able To:

  • Apply knowledge from earlier projects to solve a new, domain-specific business problem

  • Create a functional data product that demonstrates business value

  • Prepare and deliver an interview-ready STAR story that aligns with a real job description

• Dataset: Choice of three curated datasets by domain

• AI Role: Flexible—students select modality and tools