DigiFenix777 · DigiFenix777 · Aug 17, 2025 · Aug 17, 2025
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
diff --git a/README.md b/README.md
@@ -0,0 +1,181 @@
+# 📊 Data Visualization with Matplotlib and Plotly  
+
+<!-- 📛 Project Badges -->
+![Last Commit](https://img.shields.io/github/last-commit/DigiFenix777/python-crash-course-data-visualization-project)
+![Top Language](https://img.shields.io/github/languages/top/DigiFenix777/python-crash-course-data-visualization-project)
+![Repo Size](https://img.shields.io/github/repo-size/DigiFenix777/python-crash-course-data-visualization-project)
+![Issues](https://img.shields.io/github/issues/DigiFenix777/python-crash-course-data-visualization-project)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+
+
+## 🔑 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.  
+
+---
+
+## 📌 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.  
+
+---
+
+## 🧠 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).  
+
+---
+
+## 🗂️ Repository Structure  
+
+```plaintext
+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:
+```bash
+git clone git@github.com:your-username/project-data-visualization.git
+cd project-data-visualization
+```
+
+(Optional) Create a virtual environment:
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+```
+
+Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+Run any exercise script, for example:
+```bash
+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.
+
+
+---
+
+
+## 📝 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.
+
+---
+
+
+## 📊 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.
+
+---
+
+
+## 🌍 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).
+
+
+---
+
+
+## 📚 Attribution
+
+Based on exercises from:
+Matthes, E. (2023). Python Crash Course (3rd ed.). No Starch Press.
+Book website
+
+---
+
+
+## 🧩 License
+
+Distributed under the MIT License.
+See LICENSE for details.
+
+---
diff --git a/html/dice_visual_2d8.html b/html/dice_visual_2d8.html
diff --git a/images/portfolio/cubes_plot.png b/images/portfolio/cubes_plot.png
diff --git a/images/portfolio/mod_random_walk_inferno.png b/images/portfolio/mod_random_walk_inferno.png
diff --git a/images/portfolio/mod_random_walk_purples.png b/images/portfolio/mod_random_walk_purples.png
diff --git a/images/portfolio/random_walk_magma.png b/images/portfolio/random_walk_magma.png
diff --git a/images/portfolio/squares_plot.png b/images/portfolio/squares_plot.png
diff --git a/src/dice_visual_2d10_list_comp.py b/src/dice_visual_2d10_list_comp.py
@@ -23,5 +23,5 @@
 # Further customize chart.
 fig.update_layout(xaxis_dtick=1)
 
-fig.show()
+# fig.show()
 # fig.write_html('../html/dice_visual_2d8.html', '')
diff --git a/src/mod_rw_visual.py b/src/mod_rw_visual.py
@@ -1,4 +1,4 @@
-# Exercise 15.4 Modified Random Walks
+# Exercise 15-5 Modified Random Walks
 import matplotlib.pyplot as plt
 
 from modified_random_walk import ModRandomWalk

diff --git a/src/modified_random_walk.py b/src/modified_random_walk.py
@@ -1,4 +1,4 @@
-# Exercise 15-4 Modified Random Walks
+# Exercise 15-4, 15-5 Modified Random Walks
 from random import choice
 
 

diff --git a/src/molecular_motion.py b/src/molecular_motion.py
@@ -24,9 +24,9 @@
     ax.get_yaxis().set_visible(False)
 
     # Print plot to screen
-    # plt.show()
+    plt.show()
     # Save plot to file.
-    plt.savefig('15-3_molecular_motion.png', bbox_inches='tight')
+    # plt.savefig('15-3_molecular_motion.png', bbox_inches='tight')
 
     keep_running = input("Make another walk? (y/n): ")
     if keep_running == 'n':

diff --git a/src/scatter_cubes.py b/src/scatter_cubes.py
@@ -19,5 +19,5 @@
 ax.axis([0, 6_000, 0, 140_000_000_000])
 ax.ticklabel_format(style='plain')
 
-# plt.show()
-plt.savefig('cubes_plot.png', bbox_inches='tight')
+plt.show()
+# plt.savefig('cubes_plot.png', bbox_inches='tight')