Aviation operations analytics · Tampa

Every flight
leaves a trace.

Ahmed Azmy — duty manager on the ramp, analyst everywhere else. I turn what actually happens at the gate into something a dashboard can answer.

Finding 01 · FAA Wildlife Strike Database
Strikes cluster in summer and fall — not evenly across the year.
25,429 records · χ² test, p < 0.001 · seasons assigned from raw incident dates
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Most aviation analysis is written by people who have read about the operation. I have worked it.

Duty manager · TPA · Data science, University of South Florida
Ahmed Azmy Elsayed
Ahmed Azmy ElsayedTPA
0
Strike records
cleaned and analyzed
0
Rows in the largest
dataset shipped
0
Variables coded
and validated
0
GPA · Dean's list
three consecutive terms

The board

Four projects · end to end
Built with
Project
What it answers
Status
POWER BI
Guest Experience Dashboard
Which operational factor — delay, gate, agent, cabin — actually moves passenger sentiment.
Live
R · χ²
Birds vs Planes
When bird strikes concentrate, and whether the seasonal pattern is real or noise.
Live
TABLEAU
Car Sales Analysis
How mileage, body type and color predict what moves off a dealership lot.
Live
PRODUCT
Live Survey Insights
Why one post-arrival survey hides friction, and what replaces it at each touchpoint.
Concept
01 · Power BI · Voice of the Customer

Guest Experience
Dashboard

An airline gets thousands of survey responses and still can't answer the only question that matters: which part of the operation is costing us the score?

This dashboard makes that answerable in one click. Filter by flight number, cabin, gate, agent, aircraft type or issue category, and watch sentiment move against the operational facts behind it — delays, service touchpoints, check-in performance.

  • Six filter dimensions — flight, cabin, gate, agent, aircraft, issue type
  • Sentiment tied to operational cause, not just star rating
  • Built to answer the duty manager's question, not the analyst's
  • Modelled on real VOC structures used in station operations
Interactive dashboardPower BI
Airline guest experience dashboard showing sentiment filtered by operational factors
02 · R · Chi-square · FAA Wildlife Strike Database

Birds vs Planes

Everyone in operations knows bird strikes feel seasonal. This tested whether the pattern is real — and it is. Summer and fall carry significantly more strikes, at p < 0.001.

That turns a hunch into a staffing and mitigation schedule. If risk concentrates in two seasons, wildlife management and crew briefings should too.

  • 25,429 records, 29 variables — FAA strike data via Kaggle
  • Dates reformatted, seasons assigned, invalid entries removed
  • Chi-square confirmed strikes concentrate in summer and fall
  • Most strikes caused no damage — but summer severity ran higher
  • Seasonal line charts surfaced multi-year trend, not one-off spikes
Strikes by seasonR · ggplot2
Bird strikes by season chart
Damage severityR
Damage severity distribution
Trend over timeR
Bird strike trend over time
Seasonal breakdownR
Seasonal breakdown of strikes
Read the full analysis
03 · Tableau · 558,837 records

Car Sales Analysis

Half a million sales records, one question: what should a dealership actually put on the lot?

Cleaning was most of the work — body type names standardised, sale dates parsed, mileage binned into ranges a buyer would recognise. What came out was a clear inventory and marketing signal.

  • 558,837 records, 16 variables — Kaggle
  • Peak demand January–February; dips in April and July
  • SUVs and sedans dominate; coupes and hatchbacks stay niche
  • 10K–40K mile cars sell most; volume drops sharply above that
  • Neutral colours lead — grey, white, black. Bright colours lag badly
Sales dashboardTableau
Car sales Tableau dashboard
Demand by monthTableau
Monthly car sales demand
Mileage and colourTableau
Sales by mileage band and colour
View the full Tableau analysis
04 · Product concept · Survey design

Live Survey Insights

The standard airline survey fires once, by email, after arrival, and asks for a 1–10 score and a wall of text. Passengers only remember the extremes.

So the data comes back with the middle of the journey missing. You learn the flight was bad. You don't learn it was the gate change, the bag drop queue, or the boarding call nobody heard.

The fix: replace one long survey with micro-surveys at each touchpoint — two questions and an optional comment, delivered where the moment actually happened.

  • Problem — single post-arrival survey hides where friction occurred
  • Recall bias means only standout moments get reported
  • Solution — two-question micro-surveys per touchpoint
  • Check-in, lounge, boarding, arrival measured separately
  • Pinpoints the touchpoint instead of scoring the whole trip
ConceptTouchpoint survey flow
Live survey touchpoint concept
View the full project

Credentials

DataCamp · University of South Florida

Programming &
data analysis

  • Introduction to R
  • Intermediate R
  • Introduction to Python
  • Intermediate Python
  • Introduction to SQL
  • Intermediate SQL
  • Data Manipulation in SQL
  • Cleaning Data in Python

Data science &
machine learning

  • Introduction to Regression in R
  • Machine Learning with caret in R
  • Introduction to Deep Learning in Python
  • Cluster Analysis in R

Text & time
series analysis

  • Text Mining with Bag-of-Words in R
  • Introduction to Text Analysis in R
  • Time Series Analysis in Python
  • Time Series Analysis in R
  • Manipulating Time Series Data in R

Visualization &
statistics

  • Introduction to Data Visualization with ggplot2
  • Intermediate Visualization with ggplot2
  • Introduction to Statistics in R
GPA 3.90+
Honorary Dean's List — University of South Florida
Spring 2024 · Summer 2024 · Fall 2024

Data cleaning · analysis · visualization · reporting

R
PYTHON
SQL
POWER BI
TABLEAU
EXCEL

Let's talk
operations.

Nav lights rendered to spec — red to port, green to starboard,
white strobe on a one-second double flash.