Nhat Nguyen · Data Analyst

Data, Signals, and AI-ready Insights

I’m Nhat Nguyen, a data analyst who turns messy datasets into crisp, decision-ready stories. I learn fast, stay curious, and don’t quit when things get tough—every challenge is a chance to build better systems with SQL, Python, and BI.

12+ dashboards
Power BI · Tableau
7 end-to-end projects
ETL · SQL · EDA
4 case studies
Business Impact

Skills

Tableau
95%
SQL
92%
Power BI
88%
Python
85%

Projects

E-commerce Sales Performance

BI / KPI

KPI dashboard simulating sales by category, region, and segment with drill-downs.

  • Skills: KPI design, data modeling, SQL/LOD thinking
  • Impact: Flagged unprofitable SKUs; reduced returns by 8%
Live Preview Sample Dataset
DashboardKPIVisualization

Airline Delay Analysis

SQL / Python

Monthly delay percentages by carrier with seasonal worst-month insights. Click a carrier for details.

  • Skills: SQL CTEs & window functions, pandas EDA
  • Insight: Weather spikes in winter for northern hubs
Live Preview BTS On-Time
SQLpandasEDA

Data Cleaning & Automation

Python / ETL

CSV normalization (dates, categories, duplicates) with a raw vs cleaned preview.

  • Skills: pandas, regex, scheduling
  • Impact: Cut manual processing by 70%
Live Preview Kaggle Sources
PythonETLpandas

Career Goals

Data Analyst (BI-first)

Own KPIs, build executive dashboards, and communicate insights to drive product and revenue decisions.

SQL & Python Excellence

Design reliable data models, optimize queries, and automate repeatable analysis with clean, tested code.

AI-Augmented Analytics

Use AI to accelerate exploration and storytelling while keeping rigorous, human-checked conclusions.

E-commerce – Live PreviewClose
Airline Delays – Monthly % by CarrierClose
Higher % means a larger share of delayed arrivals that month.

Details

Select a carrier row to see the worst month and date per season.
Data Cleaning – Raw vs CleanedClose

Raw

Cleaned

Python-style logic 
import pandas as pd
def clean(df):
  for c in df.columns:
    if 'date' in c.lower():
      df[c] = pd.to_datetime(df[c], errors='coerce')
  for c in df.select_dtypes('object'):
    df[c] = df[c].astype(str).str.strip().str.title()
  return df.drop_duplicates()

Contact

Email: jasonnguyen101191@gmail.com