About
I am a Computer Science Graduate at Montclair State University
with a minor in Data Science. My work
focuses on understanding behavior in real datasets and presenting
results clearly rather than building models in isolation.
My coursework has involved working with real-world data,
statistical analysis, and visualization. I’ve led technical
presentations and collaborated on software projects where I
designed and implemented user-facing interfaces that interact with
backend systems.
I am particularly interested in how data
supports decisions, identifying patterns, explaining why they
occur, and turning analysis into practical tools people can use.
Technical Skills
Highlights
Education
B.S. Computer Science • Minor: Data Science
Relevant Coursework
Data Visualization • Advanced Tech in Data Science • Database Systems • Software Engineering II • Data Science & Statistics • Computer Security
Experience
IT support/repair internship • IT dept (manufacturing) internship • Home Depot (Head Cashier track)
Project Roadmap
Month 1 — Behavioral Data Analysis
Analyze a real dataset and communicate decision-oriented insights with strong visuals.
Month 2 — Predictive Modeling
Build an interpretable model that supports a decision (metrics + tradeoffs + explanation).
Month 3 — Decision Support Tool
Deploy the work into a usable tool (API + simple UI) and document it clearly.
Projects building in private
This section will grow month-by-month. Each project will include analysis, visuals, and conclusions.
I built this dashboard to track home prices in New Jersey from 2000 through 2026. The data comes from Zillow, broken down by zip code, county, and property type. You can see which counties cost the most, how values have changed since 2010, and how single-family homes compare to condos and different bedroom counts. The goal was to show real patterns: the jump after 2020, shore counties pulling ahead, and the growing price gap between counties.
The post-2020 spike is the whole story
Prices climbed through the early 2000s and dropped about 15% after the 2008 crash. Then they crept back up slowly until 2019. After 2020, prices jumped 52% in five years. More than the entire previous decade. Remote work and low inventory made being close to NYC a bigger advantage than before.
The shore and NYC corridor pulled ahead
Cape May went up 192%, Ocean 124%, and Hudson 127%. The coastal counties and areas near Manhattan saw the biggest gains. Meanwhile Hunterdon (+71%) and Somerset (+65%) grew slower. People leaned toward the beach or the city, not the traditional suburbs.
Price gaps within New Jersey got wider
Cape May averages $1.1 million. Cumberland averages $284,000. Back in 2010 the gap was about 3x, now it's nearly 5x. Affordability varies a lot depending on where you look. Inland South Jersey is still within reach for many buyers, but the coast and the NYC corridor have become much more expensive.
Bedroom count drives price more than you'd guess
A 1-bedroom property averages around $316,000. A 5-bedroom goes up to $1.26 million. Which is roughly four times higher. The jump from 4-bedroom to 5-bedroom is especially steep at 58%. That tells me 5-bedroom properties are a luxury tier, not just a bigger house. Condos average about $445,000, close to what a 2-bedroom goes for ($439,000). Condo buyers pay for location. Bedroom buyers pay for space.
New Jersey has many different housing markets, not one
Look at the dashboard and the numbers together. The statewide trend shows a clear break after 2020. The county maps show who gained most: coastal areas and towns near NYC surged while some inland suburbs barely moved. The gap between Cape May and Cumberland kept growing. Property type adds another layer. Bedroom splits affordable from luxury faster than most people realize. If you're buying, renting, or just trying to figure out where things are headed, county and property type will tell you more than any statewide average.