Kyle Dang — Data & Business Analyst

About Me

Hi, I'm Kyle.

I just graduated from SJSU in May 2026 with a B.S. in Management Information Systems, finishing Summa Cum Laude with a 3.854 GPA. I grew up in San Jose and I'm looking to start my career as a data or business analyst somewhere in the Bay Area.

During school I looked for real experience wherever I could find it. Since 2022 I've volunteered with a local clinic doing remote patient-monitoring data entry and reporting — reviewing blood pressure and heart rate readings for a roster of patients and producing the monthly vital-sign summaries clinicians actually use. It isn't glamorous work, but four years of it taught me what accuracy really costs when the data is about real people. I also spent a semester doing a systems and UX audit for a Salesforce platform that 300+ nonprofits use to manage volunteers, and delivered the recommendations straight to stakeholders.

Outside of that, I've done several independent analysis projects — a churn prediction model, a Tableau sales dashboard, NBA matchup stats — and I'm currently building Platify, an AI meal recommendation app I started from scratch after graduating. I work in Python, SQL, Excel, and Tableau, and I've been getting into AI tooling (LangGraph, RAG, vector stores) through a course I took my final semester.

I'm a fast learner, I take feedback well, and I genuinely enjoy this kind of work. If you're looking for someone entry-level who's already put in the reps, I'd love to connect.

Let's Talk LinkedIn GitHub

Experience

Where I've worked

🏥

Healthcare Data Entry

Dr. Kumo — San Jose, CA · Volunteer

Jun 2022 – Present

Review remote patient-monitoring data (blood pressure and heart rate) for an assigned roster of patients, producing monthly vital-sign summary reports for clinician review
Track and report each patient's readings and ranges consistently across the full roster every cycle — accuracy is the whole job when the data is this sensitive
Maintain and organize patient records in spreadsheets to keep recurring reporting accurate and on schedule — four years running, every cycle

🔎

System Analyst & UX Consultant — ↗ GitHub

Santa Cruz County Volunteer Center — San Jose, CA · Volunteer

Aug 2025 – Dec 2025

Audited a Salesforce-based volunteer platform used by 300+ nonprofit partners, identifying key accessibility and usability gaps
Produced redesign documentation including wireframes, WCAG 2.1 improvements, and navigation restructuring
Delivered stakeholder-facing recommendations that aligned system changes with real user needs and scalability goals

🍽️

Founder & Developer

Platify — AI Meal Recommendation App · San Jose, CA

May 2026 – Present

Building a React Native app that personalizes meal recommendations using live restaurant, nutrition, and AI APIs for users with dietary restrictions
Structured and managed 5,732 meal entries across 208 restaurant chains, with filtering logic for calories, allergens, and nutrition goals
Integrated Google Places, FatSecret, Supabase, and Claude AI APIs for real-time data — handling the full data architecture from ingestion to recommendation output

Projects

Selected work

SQL · Power BI Jul 2026

Yelp Business Analytics Dashboard

Queried and aggregated the Yelp Open Dataset in SQL, then modeled it into a Power BI dashboard covering 150K+ businesses and ~7M reviews across 27 states. Built the data model, wrote the aggregations, and designed the KPI and map views below.

Pennsylvania and Florida lead in both business count and total review volume California posts the highest average rating (4.0★) despite a smaller footprint Ratings cluster around 4.0★ — most businesses land between 3.5 and 4.5 stars State-level slicers recompute every KPI for side-by-side market comparison

SQL Power BI DBeaver Data Modeling Aggregation KPI Dashboards

View on GitHub

Yelp Power BI overview dashboard — KPIs, ratings distribution, review volume

Overview — KPIs & ratings distribution⤢ enlarge

Yelp Power BI geographic view — business density by state

Geographic view — density by state⤢ enlarge

Data Analysis Feb – Apr 2026

IBM Telecom Customer Churn Analysis

Dug into 7,043 telecom customer records to find out who leaves and why. Cleaned the data, engineered features, and ran exploratory analysis in Python to surface the patterns that separate customers who stay from customers who churn — the kind of findings a retention team can actually act on.

Month-to-month contracts had significantly higher churn than annual plans Fiber optic customers churned at 40%+ — highest of any service tier Electronic check users showed elevated risk; auto-pay users churned far less Customers without add-ons (security, support) were 2× more likely to leave

Python Pandas Seaborn Data Cleaning Feature Engineering EDA Google Colab

View on GitHub

Churn Rate by Contract Type

Month-to-Month

42%

Fiber Optic

41%

Electronic Check

36%

No Add-Ons

31%

Annual Contract

11%

7,043 customers

26.5% overall churn

5 risk segments

Business Intelligence Feb – Mar 2026

Online Retail Sales Dashboard

Turned 500K+ messy transaction records into interactive dashboards a non-technical stakeholder can actually use. The Tableau dashboard tells the full two-year story — seasonality trends, top products, geographic revenue splits — while a fully formula-driven Excel dashboard (KPI cards, dynamic top-10 rankings) drills into a single trading window, with test records and non-product entries cleaned out first.

Revenue spiked sharply in Nov–Dec — inventory planning opportunity UK represented the majority of revenue, creating geographic concentration risk Small % of customers generated disproportionate revenue — high retention priority Gift and decorative items consistently outperformed other product categories

Excel Tableau Pivot Tables Data Cleaning KPI Dashboards

View on GitHub

Monthly Revenue Trend (Indexed)

JanMarJunSepNovDec

Revenue by Region

United Kingdom

82%

Europe (other)

12%

Other Markets

AI Engineering Spring 2026 · SJSU BUS4 118S

Agentic AI — Automated IT Support System

Built a full agentic AI system that autonomously handles IT support workflows — from intake and intent classification to resolution and escalation. Led the team as project manager while also contributing to implementation.

AI agents handle password resets, software troubleshooting, and phishing detection end-to-end RAG with vector embeddings powers contextual knowledge retrieval for support responses LangGraph-style orchestration routes, escalates, and resolves tickets without human input Designed 3 full use-case workflow diagrams and a complete system architecture

Python LangGraph RAG Vector Stores Agent Orchestration Jupyter Notebook

View on GitHub

Agent Workflow — Ticket Resolution

Ticket Intake

→

Intent Classifier

RAG Retrieval

→

Agent Resolution

Escalation Check

→

Auto-Resolved

3use cases

RAGknowledge base

PM+ dev role

In Progress May 2026 – Present

Platify — AI-Powered Meal Recommender

Founded and built a React Native app that personalizes restaurant meal recommendations using live APIs and AI. Users input dietary restrictions, calorie goals, and allergens — Platify matches them to real menu items from 208 chains in real time.

Structured 5,732 meal entries across 208 restaurant chains — 100% real menu data Integrated Google Places, FatSecret, Supabase, and Claude AI for live data Custom filtering and ranking logic for calories, allergens, and nutrition goals Full-stack: data architecture, API integration, and mobile UI

React Native Claude AI API Supabase Google Places API FatSecret API JavaScript

Data Architecture

Live APIs

Google Places FatSecret Claude AI

Database

Supabase · 5,732 meal entries · 208 chains

Ranking Logic

Calories · Allergens · Macros · Preferences

Exploratory Analysis Apr 2026

NBA Head-to-Head Performance Analysis

Exploratory data analysis of 2024–25 NBA game data to evaluate how player performance shifts depending on matchup. Cleaned raw game logs and surfaced statistical patterns across opponents.

Player performance varies significantly by opponent — matchup context matters Field goals and minutes played showed strongest correlation with game outcomes Built end-to-end: raw CSV ingestion, cleaning, EDA, and documented insights

Python Pandas Jupyter Notebook EDA Data Cleaning

View on GitHub

Performance vs. Opponent (Sample)

vs. Bottom-5

+28.4

vs. Mid-Tier

+24.1

vs. Top-10

+18.7

vs. Top-3

+14.2

24–25NBA season

EDAmethodology

Data that
drives decisions.

Hi, I'm Kyle.

What I work with

Where I've worked

Selected work

Academic background

Open to analyst roles
in the Bay Area

Data thatdrives decisions.

Hi, I'm Kyle.

What I work with

Where I've worked

Selected work

Academic background

Open to analyst rolesin the Bay Area

Data that
drives decisions.

Open to analyst roles
in the Bay Area