This repository serves as a comprehensive knowledge base and use case collection for data science, artificial intelligence, machine learning, and data visualization. It covers multiple technologies, sample exercises, data preprocessing techniques, and applications across a variety of disciplines such as Bioinformatics, Deep Learning, Data Security & Analysis, and Algorithms.
- Bioinformatics
- Deep Learning
- Data Security & Analysis
- Algorithm Design
- Machine Learning
- Data Visualization
- AI
- Data Visualization
- R, Python
- Seaborn, Matplotlib
- Object-Oriented Design
- Shiny (R web applications)
- Outlier detection
- Data preprocessing
- Jupyter Notebook
- Data Visualization
- Machine Learning
- Python
- Data Visualization
- Machine Learning
Here are some essential Python libraries used in data science, AI, and machine learning that you may need:
- Python 3 Standard Library: Documentation for built-in Python features.
- NumPy: A library for numerical computations with arrays.
- Pandas: A powerful tool for data manipulation and analysis.
- Matplotlib: A popular plotting library for creating static and interactive visualizations.
- R: Free software environment for statistical computing and graphics.
- RStudio IDE: Integrated development environment (IDE) for R with powerful tools for plotting, debugging, and workspace management.
- Jupyter Notebook: An open-source web application for creating and sharing live code, visualizations, and narrative text.
- Anaconda Navigator: A desktop GUI for managing data science environments and packages for Python and R without using the command line.
- Install R - Download and install R from the official website.
- Install RStudio - Download the free version of RStudio Desktop.
-
- Jupyter Notebook - Use the free online Jupyter Notebook for testing Python code without installation.
- Khuyen Tran: Efficient Python tricks and tools for data scientists
- Truc Huynh: Bioinformatics Projects