Introduction
Python is a multi-purpose programming language that is gaining popularity in the field of data science. It is best known for its clarity, readability, and adaptability. A Large and active community of Python developers has produced several libraries and data science tools. Data import, Cleaning, Analysis, and Visualisation are made simple by these packages. Python is a solid programming language for AI/ML.
Why Learn Python for Data Science?
Python is an excellent programming language for data science for a variety of reasons. Here are a few examples:
- Python is simple to use and learn.
- A large and active development community exists for Python.
- For data science, Python includes a large selection of libraries and tools.
- Open-source and cost-free to use is Python.
Python Libraries Used in Data Science:
- NumPy: This library is used for scientific computing. It provides a high-performance multidimensional array object and a wide range of mathematical functions.
- Pandas: Essential for data manipulation and analysis, providing data structures like DataFrames.
- Matplotlib: This Matplotlib library is used for plotting the data. It provides a wide range of plotting features, that include 2D and 3D plots, Histograms, & Scatter Plots.
- Seaborn: Used in statistical plotting. It builds on Matplotlib and provides additional features for creating beautiful and informative statistical plots.
- Scikit-learn: A go-to library for machine learning, offering tools for classification, regression, clustering, & many more. TensorFlow and PyTorch: Deep learning libraries that are often employed to build and train neural networks.
- NLTK (Natural Language Toolkit) and SpaCy: For Natural Language Processing (NLP) tasks like text analysis and language understanding.
- StatsModels: Used for statistical modeling and hypothesis testing
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