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.