Data science relies on a variety of tools to process, analyze, and visualize data. Here’s a guide to the most commonly used tools in the field:
🔢 Core Programming Languages
- Python 🐍: Widely used for its simplicity and extensive libraries (e.g., Pandas, NumPy, Scikit-learn).
- R_language 🦑: Popular for statistical analysis and data visualization.
📊 Data Analysis & Visualization
- Jupyter_Notebook 📝: Interactive environment for writing and running code.
- Tableau 📊: Powerful tool for creating dashboards and visual reports.
🧠 Machine Learning Frameworks
- TensorFlow 🤖: Open-source library for building and training machine learning models.
- PyTorch 🧠: Flexible framework for deep learning and research.
📁 Data Management
- SQL 🗄️: Standard language for querying relational databases.
- MongoDB 🐸: NoSQL database for handling unstructured data.
For deeper exploration, check out our Data Science Tutorials section.