Three-day, hands-on AI workshop · 2–4 November 2026
Website (https://decoding-data-science.github.io/sharjah/)
https://decoding-data-science.github.io/sharjah/prompt-engineering-guide.html#five
https://decoding-data-science.github.io/sharjah/rag_play_lab.html
Individual Survey
https://forms.gle/SLs9ksmtvqWjK6276
Team Capstone Submission
https://forms.gle/9FcgqLh6zFyW4ZgF8
Sample prompt to improve gradio after final step
Okay, so I want to focus on airport queue staffing systems. So I want you to analyze the whole context on the top and create me a whole code or a plan to create the different tools and the SQL tool and the system prompt to focus on airport staff. Can you repeat that? Airport queue and staff assistance. So the main focus is to have an agent that helps us in staff rosters, allotment, management, and all those things. So if the Gradio app that you are building, can you please focus more on that specific aspect and create the logo, the tables, the FAQs, the headers, if you can create a status button, like green, red, yellow, a forecasting, a graph of the days that we have. So if you can create me a full nice application along with some numbers that I can showcase as a demo, it will be great.
A practical introduction to data analytics, generative AI, document understanding, and AI-assisted workflows using Google Colab and the Gemini API. The exercises follow a fictional airport operations case study known as “The Friday Problem.”
Workshop safety: All sample data and policies are fictional. Do not upload real passenger, employee, organizational, or otherwise confidential information to AI tools. Store API keys in Google Colab Secrets—not in code or this repository.
| Day | Focus | Outcomes |
|---|---|---|
| Day 1 — Explore & Ask | Data understanding, document understanding, models, system prompts, and context | Explore airport data and ask better grounded questions. |
| Day 2 — Build the Analyst | AI-assisted analysis and practical notebooks | Apply the tools to structured and unstructured workshop materials. |
| Day 3 — Put It Together | Integrated workflow and practice | Combine the skills into an end-to-end use case. |
- Read the participant handbook and workshop handout.
- Open the workshop website for the daily agenda, setup, exercises, and downloads.
- Prepare a Google account and open Google Colab.
- Obtain any required Gemini API access through Google AI Studio and keep credentials in Colab Secrets.
- Work through the Day 1 notebooks in numerical order. Open each
.ipynbin Colab and follow its setup cells. - Use the fictional airport data and document register for exercises.
The synthetic SQLite database is included as eight compressed parts under data/database/. After cloning or downloading the repository, restore the original airport_ops.db by running:
python data/database/restore_database.pyThe script checks the database integrity before writing it. The Excel workbook with 11 worksheets is available directly, without any restoration step. See the database README for details.
sharjah/
├── README.md # Start here
├── .gitignore
├── docs/ # GitHub Pages-ready workshop website
│ ├── index.html
│ ├── setup.html
│ ├── day-1.html
│ ├── day-2.html
│ ├── day-3.html
│ ├── exercises.html
│ ├── downloads.html
│ ├── assets/
│ └── downloads/ # Participant downloads released for Day 1
├── notebooks/ # Day 1 Colab notebooks and Python helpers
│ ├── D1_01_Data_Understanding.ipynb
│ ├── D1_02_Documents_Understanding.ipynb
│ ├── D1_03_Models_System_Prompt_Context.ipynb
│ ├── workshop_helpers.py
│ └── workshop_agent.py
├── data/
│ ├── database/
│ │ ├── airport_ops.db # Rebuild with restore_database.py
│ │ └── Data_Dictionary.md
│ └── documents/ # Fictional policy documents in TXT/PDF
├── slides/ # Day 1 presentations
├── handouts/ # Participant PDFs
├── release_later/ # Day 2/3 material: publish on schedule
├── source/ # Workshop content-generation scripts
└── index_single_file.html # Portable, single-file version of the site
Note: The folder tree describes the full workshop distribution. If some assets are not yet available in GitHub, use the original workshop bundle until they are published.
| Resource | Location |
|---|---|
| Website and day-by-day guidance | docs/ |
| Colab notebooks | notebooks/ |
| Synthetic database and dictionary | data/database/ |
| Fictional policies and document register | data/documents/ |
| Presentation slides | slides/ |
| Participant handbook and handout | handouts/ |
| Day 2 and 3 release materials | release_later/ |
| Site and resource generator scripts | source/ |
The prepared website uses plain HTML, CSS, and JavaScript—no site build is required.
- In GitHub → Settings → Pages, choose Deploy from a branch.
- Choose branch main and folder /docs.
- Save. Once the site is deployed, visit https://decoding-data-science.github.io/sharjah/.
Release policy: The Day 2 and Day 3 participant assets live in release_later/. Copy each day's intended materials into docs/downloads/ only when that day's release is approved. Avoid publishing facilitator solutions or answer keys.
- Use only the synthetic airport database and fictional policy documents provided.
- Confirm the effective/current document version; some included policies are clearly marked DRAFT or SUPERSEDED for learning purposes.
- Never commit API keys, service-account credentials, or private data.
- Review model names, pricing, quota limits, and access requirements before running the examples.
- Keep the workshop's public participant materials separate from facilitator-only resources.
Created by Decoding Data Science for hands-on AI education and practical enterprise use cases.
Educational use: The exercises and airport scenario are fictional demonstrations, not operational aviation guidance.