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๐Ÿ“Š Data Science and Statistics

Altair

  • Description: Declarative statistical visualization library for Python.

  • Use Case: Creating clear and effective statistical visualizations.

  • Documentation: Altair Documentation

  • GitHub Repository: Altair GitHub

Apache Spark

  • Description: Unified analytics engine for large-scale data processing.

  • Use Case: Handling big data processing and analytics, often used with PySpark, the Python API for Spark.

  • GitHub Repository: Apache Spark GitHub

Bokeh

  • Description: A library for creating interactive visualizations for modern web browsers.

  • Use Case: Building complex interactive visualizations for data exploration and presentation.

  • Documentation: Bokeh Documentation

  • GitHub Repository: Bokeh GitHub

CatBoost

  • Description: An open-source gradient boosting on decision trees library.

  • Use Case: Efficient and powerful categorical data handling for machine learning tasks.

  • Documentation: CatBoost Documentation

  • GitHub Repository: CatBoost GitHub

Dask

  • Description: Parallel computing library that scales the existing Python ecosystem.

  • Use Case: Scalable analytics that seamlessly works with Numpy, Pandas, and Scikit-Learn.

  • Documentation: Dask Documentation

  • GitHub Repository: Dask GitHub

Dash by Plotly

  • Description: A Python framework for building analytical web applications.

  • Use Case: Creating interactive, web-based data dashboards.

  • Documentation: Dash Documentation

  • GitHub Repository: Dash GitHub

H2O

  • Description: Open-source, in-memory, distributed, fast, and scalable machine learning and predictive analytics platform.

  • Use Case: Performing machine learning tasks on large datasets.

  • Documentation: H2O Documentation

  • GitHub Repository: H2O GitHub

Jupyter Notebook

  • Description: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

  • Use Case: Interactive computing and data visualization, ideal for exploratory data analysis.

  • Documentation: Jupyter Documentation

  • GitHub Repository: Jupyter Notebook GitHub

Keras

  • Description: An open-source software library that provides a Python interface for artificial neural networks.

  • Use Case: Designing and deploying deep learning models.

  • Documentation: Keras Documentation

  • GitHub Repository: Keras GitHub

LightGBM

  • Description: A gradient boosting framework that uses tree-based learning algorithms.

  • Use Case: Highly efficient and scalable machine learning, especially for large-scale data.

  • Documentation: LightGBM Documentation

  • GitHub Repository: LightGBM GitHub

Matplotlib

  • Description: A comprehensive library for creating static, animated, and interactive visualizations in Python.

  • Use Case: Data visualization and graphical plotting.

  • GitHub Repository: Matplotlib GitHub

NumPy

  • Description: The fundamental package for numerical computation in Python.

  • Use Case: Handling numerical operations essential in data processing and analysis.

  • Documentation: NumPy Documentation

  • GitHub Repository: NumPy GitHub

Pandas

  • Description: A powerful data analysis and manipulation library.

  • Use Case: Data cleaning, transformation, and analysis.

  • Documentation: Pandas Documentation

  • GitHub Repository: Pandas GitHub

Plotly

  • Description: An interactive graphing library for Python.

  • Use Case: Interactive data visualization

and dashboards.

PyCaret

  • Description: An open-source, low-code machine learning library in Python that automates machine learning workflows.

  • Use Case: Simplifying the machine learning workflow for complex tasks.

  • Documentation: PyCaret Documentation

  • GitHub Repository: PyCaret GitHub

Scikit-learn

  • Description: A machine learning library in Python.

  • Use Case: Implementing machine learning algorithms including classification, regression, clustering, and dimensionality reduction.

  • GitHub Repository: Scikit-learn GitHub

SciPy

  • Description: A Python-based ecosystem of open-source software for mathematics, science, and engineering.

  • Use Case: Scientific and technical computations.

  • Documentation: SciPy Documentation

  • GitHub Repository: SciPy GitHub

Seaborn

  • Description: A statistical data visualization library based on Matplotlib.

  • Use Case: Creating attractive and informative statistical graphics.

  • Documentation: Seaborn Documentation

  • GitHub Repository: Seaborn GitHub

Statsmodels

  • Description: A Python module that allows users to explore data, estimate statistical models, and perform statistical tests.

  • Use Case: Statistical modeling and hypothesis testing.

  • GitHub Repository: Statsmodels GitHub

TensorFlow

XGBoost

  • Description: An optimized distributed gradient boosting library.

  • Use Case: Efficient and scalable machine learning with gradient boosting.

  • Documentation: XGBoost Documentation

  • GitHub Repository: XGBoost GitHub

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