# Data Science Libraries - Python

Python has gained widespread acceptance as a language of choice for **Data Science,** while also excelling as a versatile general-purpose programming language. With its rapid ascendancy, Python has established dominance in the field of Data Science applications and **Machine Learning.** Its inherent simplicity, multifaceted nature, and excellent readability make it an evident frontrunner for Data Science endeavors.

The primary objective of this course is to offer comprehensive insights into various **Data Science Libraries,** supplemented by practical examples showcasing their application across diverse data science scenarios.

## Python computing libraries

- NumPy (Arrays & matrices)
- Panda (Data Structures & Tools)
- SciPy (Integrals, solving differential equations, optimization)

## Python Visualization Libraries

- Matplotlib (Plots and Graphsr)
- Seaborn(Plots: heatmaps, time series, violin plots)

## Python Algorithmic Libraries

- Scikit Learn (Machine Learning: regression, classification, clustering)
- Statsmodels (Explore data, estimate statistical model and perform statistical tests)

The course commences by explore into fundamental libraries essential for Python-based Data Science. For beginners, installing individual Data Science libraries can prove challenging; thus, we advocate using **Anaconda** —an all-inclusive installer. Anaconda stands as the World's Most Popular Data Science Platform, compatible with Mac, Windows, and Linux, facilitating large-scale data processing and scientific research computing in Python. It encompasses crucial scientific research and data analysis libraries like Pandas, Numpy, Matplotlib, scikit-learn, alongside IPython, making it the recommended package for this course.

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