Aviation operations analytics · Tampa

Every flight
leaves a trace.

Ahmed Azmy — Duty Manager in the Terminal, Analyst everywhere else. I turn what actually happens at the airport 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
Scroll
0
Strike records
cleaned and analyzed
0
Rows in the largest
dataset shipped
0
Variables coded
and validated

Most aviation analysis is written by people who have read about the operation. I have worked it.

Duty manager · TPA · Data science, USF More about me →
Ahmed Azmy Elsayed
01 · Monte Carlo simulation · Turnaround operations

The Buffer That
Pays For Itself

Every extra minute of schedule buffer or ground-crew staffing costs the airline money. Every minute short of it risks a delay that cascades into the next rotation. The question is never "more or less" — it's which minutes are actually worth it.

This models a real turnaround as what it actually is: a chain of dependent tasks — deboarding, cabin clean, catering, fueling, baggage, boarding — each with its own realistic timing and variability. Run it enough times and a clear, actionable picture forms of exactly where a station's time and staffing are best spent.

  • Monte Carlo model — thousands of simulated turns per setting, not one guess
  • Built on typical industry turnaround benchmarks, tunable to any station's own numbers
  • Shows exactly which task is the bottleneck — and what fixing it is worth
  • Same question ops planning already asks — answered with a live model instead of a hunch
Turnaround simulatorTypical scenario
On-time
probability
Avg delay
if late (min)
Most common
bottleneck
Open the simulator →
02 · Interactive dashboard · Airport performance

Airport Performance
Dashboard — APD

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?

Six filters — flight number, cabin, gate, agent, aircraft type, issue category — recompute every KPI and chart live, tying sentiment back to 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
  • Originally prototyped in Power BI, rebuilt here as a standalone JS dashboard
Airport Performance DashboardHeadline numbers
Customer satisfaction
(CSAT, 1–5)
Net promoter score
(NPS, 0–5)
Average delay
(minutes)
Check-in
Boarding
Responses
logged
Open the live dashboard →
03 · 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
04 · Side project · Real BTS/DOT data, 2005–2023

It Wasn't The Weather

Weather gets blamed for most delays. Nineteen years of the government's own data says the bigger factor, most years, is simpler: the previous flight on the same tail running late.

A quick look at what the numbers actually show — and the case for why buffer and staffing, not the forecast, are usually the more useful thing to plan around.

  • 16 of 19 years — late-arriving aircraft was the #1 delay cause nationwide
  • Weather hit a record low of 3.4% of delay minutes in 2023
  • 122.5M arriving flights, 2005–2023 — U.S. DOT / Bureau of Transportation Statistics
Share of arrival-delay minutes, by causeBTS · 2005–2023
Source: U.S. Department of Transportation, Bureau of Transportation Statistics — Airline On-Time Statistics and Delay Causes, transtats.bts.gov/ot_delay/OT_DelayCause1.asp (as of July 2024). Figures are each cause's share of arrival-delay minutes across all U.S. mainline reporting carriers, domestic scheduled service — nationwide, not specific to any single airport or airline.
05 · 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 — a different two-second rating, matched to each moment
  • Check-in, lounge, boarding, arrival measured separately
  • Pinpoints the touchpoint instead of scoring the whole trip
ConceptTwo channels, mocked up live — phone push and seatback IFE
9:41
Tuesday, October 14
✈️Flight Pulsenow
How was check-in?
One tap — takes two seconds.
👍👎
✈️Flight Pulse22m
Rate the lounge, quickly
How was your time before boarding?
😕😐🙂😄
✈️Flight Pulse41m
Was the boarding call clear?
Tap a star before you find your seat.
★★★★☆
✈️Flight Pulse2h
Bags out on time?
Last one — welcome home.
YesNo
01 · Check-in
"Was the line what you expected?"
👍👎
Binary, one thumb — fired the moment they leave the counter, while the queue is still fresh.
02 · Lounge
"How was your time before boarding?"
😕😐🙂😄
A mood scale, not a number — the lounge is an experience, not a transaction.
03 · Boarding
"Was the boarding call clear?"
★★★★☆
Star rating for a moment passengers already rate instinctively.
04 · Arrival
"Bags out on time?"
YesNo
Yes/no closes the loop — the one moment worth a single clear answer.
IFE · seatback screen, same idea
Same rating logic, embedded in the seat in front of you instead of a phone.
IFESeat 14A
Welcome aboard, Ahmed Azmy
At boarding
How's your seat & legroom?
★★★★☆
IFETPA → AAZ
Cabin appearance
Cabin looking sharp today?
👍👎
IFETPA → AAZ
After meal service
How was the meal?
😕😐🙂😄
IFETPA → AAZ
After refreshments
Get everything you needed?
YesNo
Same PNR link that already puts your name on the welcome screen could trigger these — tied to seat and cabin, mid-flight, not just one survey after landing.
Concept mockup, built natively for this site — not a live product. The idea: match the rating UI to the touchpoint instead of asking one long survey to do all the work.

Everything, at a glance

Five projects · end to end
Built with
Project
What it answers
Status
MONTE CARLO
The Buffer That Pays For Itself
Which minutes of turnaround buffer or staffing actually protect on-time departure.
Live
INTERACTIVE
Airport Performance 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
JS · CANVAS
It Wasn't The Weather
What 19 years of real BTS delay-cause data says actually delays a flight.
Live
CONCEPT
Live Survey Insights
Why one post-flight survey misses where the trip actually went wrong.
Concept

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
3.94 GPA
At the University of South Florida — Information Science, Data Sci & Analytics
Dean's List — 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.