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DATA VISUALIZATION WITH PYTHON

Description This project is a structured, hands-on learning journey focused on mastering data visualization in Python using NumPy, Pandas, and Matplotlib. It covers everything from basic numerical operations to real-world, business-oriented data visualization using datasets from Kaggle.

Project Structure

lesson_plan.md → The main study plan outlining all lessons and objectives for the entire project.

Lessons/ → Contains markdown (.md) files for each lesson. → Each file includes summarized notes, concepts, and code explanations. Example:

Lessons/
├── Lesson1_Numpy.md
├── Lesson2_Pandas.md
├── Lesson3_Matplotlib_Basics.md
└── ...

Practice/ → Contains practical exercises and datasets per lesson. → Each lesson has its own subfolder with Jupyter notebooks and CSV data files. Example:

Practice/
├── Lesson1/
│   └── numpy_practice.ipynb
├── Lesson2/
│   └── pandas_practice.ipynb
├── Lesson3/
│   └── matplotlib_basics.ipynb
└── ...

Learning Objectives

  1. Refresh knowledge of NumPy for numerical data handling.
  2. Learn Pandas for reading, cleaning, and analyzing tabular data.
  3. Master Matplotlib for creating line charts, bar graphs, pie charts, histograms, and scatter plots.
  4. Combine Pandas and Matplotlib to visualize real-world Kaggle datasets.
  5. Develop a mini project analyzing and visualizing business data.

Kaggle Integration

Real datasets from Kaggle are used for all lessons and projects. Suggested datasets include:

  • Supermarket Sales Dataset (Retail & Sales Analysis)
  • Apple or Tesla Stock Data (Trend Visualization)
  • Customer Personality Analysis (Marketing Insights)
  • E-commerce Data (Product & Category Comparisons)

Getting Started

  1. Clone or download this repository.
  2. Create a Python virtual environment and install dependencies: pip install numpy pandas matplotlib jupyter
  3. Open Jupyter Notebook: jupyter notebook
  4. Open the Practice folder and start exploring each lesson’s exercises.

Recommended Tools

• Python 3.10+ • Jupyter Notebook or JupyterLab • Kaggle account for dataset downloads

Mini Project

The final project focuses on creating a complete visualization dashboard using the Supermarket Sales dataset. Key tasks include:

  • Loading and cleaning data with Pandas
  • Computing key metrics using NumPy
  • Visualizing trends, comparisons, and distributions with Matplotlib
  • Presenting business insights with annotations and styled charts

Goal

By the end of this project, you’ll be confident in using NumPy, Pandas, and Matplotlib to transform raw data into meaningful business insights through clear and professional visualizations.