This document is intended to help leadership review, shape, and approve a work-related capstone project for my Data Technology program. My goal is to complete a project that satisfies the program requirements while also creating something useful for the business.
The capstone must follow a four-step process:
Because of that structure, the strongest project would be one that uses a manageable internal dataset, answers a real business question, and can be shared externally only in an anonymized or redacted form if approved.
My capstone project is expected to demonstrate that I can:
The final project is meant to tell a clear story: define a problem, explain the data and methodology, answer business questions with analysis and visuals, and summarize recommendations or conclusions.
The first part of the capstone is selecting a dataset or combination of datasets that are relevant, interesting, and manageable in size. The data should be large enough to support meaningful analysis, but not so large that it becomes difficult to clean, explore, and analyze within the course timeline.
The school recommends datasets that are ideally under 20 MB, with at least a few hundred rows and at least five columns. More detail is better, but extremely large datasets are discouraged.
For this project, I would prefer to use internal business data because it would allow the capstone to create real value for the company while also meeting the course requirements.
Leadership input would help determine:
Once a dataset is selected, the next phase is cleaning and preparing the data, then exploring it through EDA.
This step includes:
This step is important because the project will be graded not just on the final insights, but also on whether the cleaning and exploratory process is thorough, thoughtful, and well documented.
The analysis phase is the core of the capstone. This is where I would answer the business questions that guided the project.
Depending on the topic, this could include:
This is the stage where the project needs to move beyond reporting and into interpretation. The goal is not just to describe what happened, but to explain what the findings may mean for the business.
The final phase is turning the analysis into a polished portfolio piece. The school expects the project to be made publicly accessible through GitHub or a website and to include a structured final report.
That means the project must ultimately include:
If company data is used, the public version would need to be anonymized, redacted, or otherwise adapted so that it protects confidential information.
The strongest current idea is a marketing and e-commerce analysis focused on customer quality and channel performance.
Marketing Channel Performance and Customer Value Analysis for E-commerce Growth
Which marketing channels and campaign types generate the most valuable customers for the business?
This topic is a good fit because it goes beyond surface-level reporting and looks at both immediate performance and longer-term customer value. It also aligns well with the skills covered in the program, including data cleaning, analysis, visualization, and business storytelling.
This topic could help answer questions such as:
Below are several capstone directions leadership could approve or refine.
Main question: Which channels and campaign types bring in the most valuable customers?
Potential data sources:
Potential outputs:
Main question: Which customer segments are most associated with repeat purchasing and long-term value?
Potential data sources:
Potential outputs:
Main question: Which products, product categories, or starter kits drive the strongest revenue contribution and follow-on purchasing behavior?
Potential data sources:
Potential outputs:
Main question: Which event-related efforts or lead sources appear to drive stronger registrations, attendance, or downstream value?
Potential data sources:
Potential outputs:
To stay within school expectations and keep the project manageable, the selected data should ideally be:
A practical project may use one primary dataset or combine a few related exports into a single analysis-ready dataset.
If the marketing and customer value project is approved, likely useful fields would include:
Because this project may involve internal business data, I want to be careful about confidentiality and external sharing.
My intent would be:
If approved, this project could create value beyond the school requirement by producing a structured analysis that may help with:
The goal is to create something that is academically valid and also practically useful.
I would appreciate leadership input on the following:
If leadership is open to this, the next step would be to choose one project direction and define:
I would like this capstone to be more than a school exercise. My hope is to use it as an opportunity to create a thoughtful and useful analysis that supports the business while also meeting the academic requirements of my Data Technology program.