Projects

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

You won'tt just learn about AI and data science—you will 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 you to clearly communicate your 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. 

Project Summaries:

• 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: Recommending New Office Locations (Management Consulting: Location Strategy) 
  Students will use AI and traditional analytics (Exploratory Data Analysis, or EDA) to explore and combine large location datasets and recommend the best cities for new office locations for a financial services company.

• 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: Reverse Pitch – Create value by solving a business problem
  Businesses present a problem, which groups are tasked with solving. Students will work in teams, to create a machine learning model or AI agent that solves a business problem, culminating in them giving their pitch to the business. 

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: Management Consulting - Location Strategy

• Business Case: A company is looking for the best cities in which to open new offices.

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

• Students Will Be Able To:

  • Use AI tools alongside traditional methods to explore and combine large, complex datasets

  • Identify and explain data-driven insights and areas for future exploration

  • Visualize findings and communicate business value

• Datasets: 1. US Counties, 2. CDC: 500 cities, 3. AdvisorSmith: Cost of living, 4. Golden Oak: Income, 5. FEMA: Risks

• 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: Reverse Pitch – Create value by solving a business problem

• Business Case: Students choose from a real-world business problem (finance, health, web/sales).

• Output Modality: ML model or AI agent with interview-style presentation is created.  Students use it to pitch their solution to the business.

• 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 problems/datasets by domain

• AI Role: Flexible—students select modality and tools