🎓 MSDS Capstone Project

Master of Science in Data Science — Capstone Course

A culminating project demonstrating business strategy, data modeling, and technical implementation across a real-world use case.

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📌 Project Overview

This repository contains the full deliverables for our MSDS Capstone project, completed as part of the final capstone requirement for the Master of Science in Data Science program. The project integrates skills across the three core pillars of the MSDS curriculum:

Pillar	Focus Areas
📊 Business	Strategic thinking, consulting, stakeholder communication, business planning
🤖 Modeling	Statistical analysis, machine learning, model evaluation, and insights
💻 Information Technology	Data pipelines, system design, implementation, and deployment

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🏢 Business Case

Briefly describe the industry, problem statement, and business context here.

• Industry: [e.g., Healthcare / Finance / Retail / etc.]
• Problem Statement: [1–2 sentences describing the core business problem]
• Strategic Objective: [What competitive or operational advantage does this project deliver?]

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👥 Team Members

Name	Role
Name	Project Lead / Business Strategy
Name	Data Engineer / Pipeline Development
Name	ML Modeling & Evaluation
Name	Visualization & Communication

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🗂️ Repository Structure

📦 capstone-project/
├── 📁 data/
│   ├── raw/                  # Original, unmodified data sources
│   ├── processed/            # Cleaned and transformed datasets
│   └── external/             # Third-party or supplementary data
├── 📁 notebooks/
│   ├── 01_eda.ipynb          # Exploratory Data Analysis
│   ├── 02_preprocessing.ipynb
│   ├── 03_modeling.ipynb
│   └── 04_evaluation.ipynb
├── 📁 src/
│   ├── data/                 # Data ingestion and processing scripts
│   ├── models/               # Model training and inference code
│   └── utils/                # Helper functions and utilities
├── 📁 reports/
│   ├── business_plan.pdf     # Business case and strategic plan
│   ├── implementation_plan.pdf
│   └── final_presentation.pdf
├── 📁 dashboards/            # Visualization and reporting artifacts
├── requirements.txt
├── environment.yml
└── README.md

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🔬 Methodology

1. Business Understanding — Defined the problem scope, KPIs, and success criteria in collaboration with stakeholders.
2. Data Acquisition & Engineering — Identified, collected, and built pipelines for all relevant data sources.
3. Exploratory Data Analysis — Uncovered patterns, anomalies, and key relationships within the data.
4. Modeling — Developed, trained, and iterated on predictive/analytical models.
5. Evaluation — Assessed model performance against business-defined success metrics.
6. Implementation Planning — Outlined a deployment strategy, organizational considerations, and scalability roadmap.
7. Communication — Delivered findings to both technical and non-technical audiences.

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📈 Key Results

Summarize your primary findings and outcomes here.

• [Result 1 — e.g., Achieved XX% accuracy on holdout set]
• [Result 2 — e.g., Identified $XM in potential cost savings]
• [Result 3 — e.g., Reduced processing time by XX%]

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🛠️ Tech Stack

Category	Tools
Languages	Python, SQL
Data Processing	Pandas, NumPy, PySpark
Modeling	Scikit-learn, XGBoost, TensorFlow / PyTorch
Visualization	Matplotlib, Seaborn, Plotly, Tableau
Infrastructure	AWS / GCP / Azure, Docker
Version Control	Git, GitHub

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⚙️ Getting Started

Prerequisites

• Python 3.9+
• Conda or virtualenv

Installation

# Clone the repository
git clone https://github.com/your-org/capstone-project.git
cd capstone-project

# Create and activate environment
conda env create -f environment.yml
conda activate capstone

# Or using pip
pip install -r requirements.txt

Running the Project

# Run data preprocessing
python src/data/preprocess.py

# Train the model
python src/models/train.py

# Launch the dashboard (if applicable)
python dashboards/app.py

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📄 Deliverables

• Business Plan
• Project Implementation Plan
• Exploratory Data Analysis Report
• Model Documentation
• Final Presentation Deck
• Executive Summary

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🙏 Acknowledgments

We would like to thank our course instructors, program faculty, and any industry partners or mentors who supported this project throughout the MSDS Capstone course (Section 55).

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📬 Contact

For questions or collaboration inquiries, please reach out to the project team via GitHub Issues or the contact information below.

Team Member	Email
Joshua Pasaye	joshuapasaye2027@u.northwestern.edu

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This project was completed in partial fulfillment of the requirements for the Master of Science in Data Science program.