Reasons for Python's popularity in data science:
1. Easy to learn: Python has a simple syntax, making it accessible to beginners. 2. Versatile: Python can handle various data science tasks, from data wrangling to machine learning. 3. Large community: Extensive libraries (e.g., NumPy, pandas, scikit-learn) and frameworks (e.g., TensorFlow, Keras). 4. Fast development: Rapid prototyping and development capabilities. 5. Cross-platform: Runs on Windows, macOS, and Linux. 6. Extensive libraries and tools: - Data manipulation: pandas, NumPy - Machine learning: scikit-learn, TensorFlow - Data visualization: Matplotlib, Seaborn - Statistical analysis: Statsmodels 7. Integration: Seamlessly integrates with other tools and languages.
Industries using Python for data science:
1. Finance and banking
2. Healthcare
3. Retail and e-commerce
4. Social media and online services
5. Scientific research
6. Government
7. Manufacturing and logistics