This repository is a comprehensive course on data visualization, covering essential topics and their implementations. The course is designed to provide both theoretical understanding and practical skills in various data visualization techniques.
This comprehensive course covers fundamental to advanced data visualization techniques using Python. Students will learn to create effective, interactive, and diverse visualizations for various data types, including numeric, categorical, time series, image, and 3D data. The curriculum emphasizes practical application, starting with basic plotting and progressing to advanced techniques like interactive dashboards and machine learning visualizations. By the end of the course, students will have the skills to create clear, accurate, and impactful visualizations that effectively communicate data insights across various domains of data science.
- 🟢 Introduction to Data Visualization
- 📊 Visualizing Numeric & Categorical Data
- 📈 Advanced Plotting with Matplotlib & Seaborn
- 🕒 Visualizing and Analyzing Time Series Data
- 🌐 Interactive Visualization with Plotly
- 🖼️ Image Data Visualization
- 🛠️ 3D Data Visualization
- 🤖 Data Visualization for Machine Learning
- 🤖 Advanced Techniques and Real-World Applications in Data Visualization
- 🖥️ Building Interactive Dashboards for Data Science
- Clone the Repository:
git clone https://github.com/qazimsajjad/Data-Visualization.git
- Navigate to the Directory:
cd Data-Visualization - Install Dependencies:
pip install requirements.txt
By following this course, you will be able to:
- Understand the importance and principles of data visualization in data science.
- Use Python libraries like Matplotlib, Seaborn and Plotly to create effective visualizations.
- Apply visualization techniques to numeric, categorical, time series, image, and 3D data.
- Design and implement interactive visualizations using Plotly.
- Apply data visualization techniques to enhance understanding and interpretation of Machine Learning models.
The repository consists of folders with names lecture01, lecture02 and so on. In each directory you will get a lecture in pdf and jupyter notebook(s) which contains the code of the lab.
Imran Nawar
Research Assistant, Digital Image Processing (DIP) Lab
Department of Computer Science
Islamia College Peshawar (Public Sector University), Pakistan