📊 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.

• Documentation: Apache Spark Documentation

• 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.

• Documentation: Matplotlib Documentation
• 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.

• Documentation: Plotly Documentation
• GitHub Repository: Plotly GitHub

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.

• Documentation: Scikit-learn Documentation
• 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.

• Documentation: Statsmodels Documentation
• GitHub Repository: Statsmodels GitHub

TensorFlow

• Description: An end-to-end open-source platform for machine learning.

• Use Case: Building and training machine learning models.

• Documentation: TensorFlow Documentation
• GitHub Repository: TensorFlow GitHub

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