📊 Data Visualization with Matplotlib and Plotly

🔑 Executive Summary

This project explores Python-powered data visualization through Matplotlib and Plotly, focusing on both static and interactive charts. While the examples center on mathematics and simulations (cubes, random walks, dice rolls), the same visualization techniques directly apply to cybersecurity workflows, such as:

• Charting attack frequency trends over time.
• Mapping geographic spread of incidents.
• Creating interactive dashboards for SOC and compliance reporting.
• Communicating risk insights to executives and auditors with clarity.

👉 For recruiters and hiring managers: this project demonstrates my ability to transform raw data into meaningful, visual insights—a critical competency for cybersecurity analysis, reporting, and decision-making.

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📌 Overview

This project covers end-to-end data visualization practices using Matplotlib (static plots) and Plotly (interactive plots). Key topics include:

• Plotting simple line and scatter graphs.
• Customizing styles, labels, colors, and tick marks.
• Using colormaps for clarity in large datasets.
• Saving plots automatically for portfolio inclusion.
• Simulating and visualizing random walks.
• Building interactive dice roll simulations with Plotly.

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🧠 Skills & Concepts

• Matplotlib basics (line plots, scatter plots, colormaps).
• Plotly Express for interactive charting.
• Styling and scaling plots for readability.
• Object-oriented design with custom classes (RandomWalk, Die).
• Automating workflows with refactoring and list comprehensions.
• Professional Git/GitHub practices (branching, version control, repo hygiene).

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🗂️ Repository Structure

data-visualization-project/
│
├── data/                 # Input datasets or simulation outputs (empty)
│ 
├── html/                 # Exported Die Roll plots in HTML format
│   ├── dice_visual_2d8.html
│   └── dice_visual_d6d10.html
│
├── images/portfolio/     # Exported static and interactive plots
│   ├── cubes_plot.png
│   ├── mod_random_walk_inferno.png
│   ├── mod_random_walk_purples.png
│   ├── random_walk_magma.png
│   ├── squares_plot.png
│   └── dice_multiplication.png
│
├── src/                  # Python scripts for lessons & exercises
│   ├── dice_visual.py
│   ├── dice_visual_2d8.py
│   ├── dice_visual_3d6.py
│   ├── dice_visual_d6d10.py
│   ├── dice_visual_multiply_2d6.py
│   ├── die.py
│   ├── die_visual.py
│   ├── main.py
│   ├── mod_rw_visual.py
│   ├── modified_random_walk.py
│   ├── molecular_motion.py
│   ├── molecular_visual.py
│   ├── mpl_squares.py
│   ├── random_walk.py
│   ├── rw_plotly.py
│   ├── rw_visual.py
│   ├── scatter_cubes.py
│   ├── scatter_squares.py
│   └── main.py
│
├── tests/
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt

🚀 How to Run

Clone this repo and move into the folder:

git clone git@github.com:your-username/project-data-visualization.git
cd project-data-visualization

(Optional) Create a virtual environment:

python3 -m venv .venv
source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Run any exercise script, for example:

python src/scatter_cubes.py

🔎 Skills Spotlight

This project highlights:

• Python scripting for data visualization.
• Matplotlib & Plotly for both static and interactive charts.
• Designing simulations with object-oriented programming.
• Applying clean repo structure and GitHub portfolio practices.
• Building transferable skills for cybersecurity dashboards and incident reports.

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📝 Lessons Learned

• Importance of styling and scaling plots for readability and accuracy.
• How random walks simulate unpredictable patterns (mirroring attacker behavior in networks).
• How dice simulations parallel probability and risk assessment in cybersecurity.
• Why reproducibility (requirements, clear structure, README) matters for collaboration and audits.
• How visualization bridges raw technical data and executive decision-making.

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📊 Exercises Implemented

• 15-1: Cubes → Plotted cubic numbers (5 & 5,000 points).
• 15-2: Colored Cubes → Applied a colormap to cubes plot.
• 15-3: Molecular Motion → Simulated pollen grain motion with line plots.
• 15-4: Modified Random Walks → Adjusted parameters to explore behavior.
• 15-5: Refactoring → Simplified fill_walk() for readability and maintainability.
• 15-6: Two D8s → Simulated and plotted results of rolling two 8-sided dice.
• 15-7: Three Dice → Simulated rolling three D6 dice (min=3, max=18).
• 15-8: Multiplication → Visualized results of multiplying two dice rolls.
• 15-9: Die Comprehensions → Used list comprehensions to simplify dice simulations.

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🌍 Data Sources

Synthetic datasets generated in exercises (random walks, dice rolls).

Exercises and examples adapted from Python Crash Course, 3rd Edition by Eric Matthes (No Starch Press).

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📚 Attribution

Based on exercises from: Matthes, E. (2023). Python Crash Course (3rd ed.). No Starch Press. Book website

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🧩 License

Distributed under the MIT License. See LICENSE for details.

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