DataSci Internship @ Jamix · 2026

Higher Education Market Intelligence Dashboard

A portfolio-safe case study from my Data Science Internship at Jamix, using Python and Tableau to segment higher-education institutions by enrollment, financial capacity, and student demographics.

Python Pandas NumPy Tableau Excel CRM Management

Project scope

280 synthetic institution records in the public-safe version

6 segmentation dimensions used for market comparison

4 K-Means clusters used to group similar institutions

0 real clients, leads, or internal recommendations exposed

Data Pipeline

The original workflow combined public institutional data from the College Scorecard API, foodservice context from NACUFS, and operational records exported from HubSpot.

I cleaned and joined the sources in Python, standardized school names and comparable fields, validated missing or inconsistent records, and prepared analysis-ready tables for Tableau.

Analytical Goal

The project identified which current Jamix clients were most similar to potential new clients, giving the company a more evidence-based way to prioritize outreach and focus on stronger-fit opportunities.

Dashboard Preview

Portfolio-safe visuals recreated with synthetic data.

Synthetic enrollment distribution bar chart — Enrollment distribution with a mock reference subset.

Synthetic institution type coverage chart — Institution type coverage across Carnegie-style profiles.

Synthetic financial capacity curve — Financial capacity curve with the long-tail pattern preserved.

Synthetic 3D market cluster plot — 3D clusters using enrollment, endowment, and tuition revenue per FTE.

Synthetic opportunity network graph — Network view of mock institutions across analytical methods.

Confidentiality note: this page is reconstructed for portfolio use. It does not include real company data, real client names, sales-qualified leads, exact counts, internal recommendations, or original report screenshots.

What I Built

The project transformed messy institutional and customer records into decision-ready market segments for comparison, filtering, and strategic analysis.

Skills Used

College Scorecard API integration, NACUFS data preparation, HubSpot export cleaning, Python joins, feature standardization, K-Means clustering, similarity scoring, Tableau dashboarding, and network visualization.