Popular Data Science Packages

Last updated July 05, 2023

Below is a list of popular packages used for data science applications. An example of how to install packages on CARC systems using conda can be found on the Building a Customized Conda Environment page.

It is still important to check each package’s website documentation for installation instructions. Sometimes installation instructions change—the most updated processes will be on the package website.

0.0.1 Tensorflow

TensorFlow is an open-source deep learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow supports both CPU and GPU acceleration, making it suitable for a wide range of computational environments.

Key features:

  • High-level APIs for building and training neural networks
  • Flexible architecture allowing easy deployment on different platforms
  • Support for distributed computing and scaling up training on multiple devices
  • Integration with other deep learning libraries like Keras for rapid prototyping

0.0.2 PyTorch

PyTorch is a popular open-source deep learning framework widely used for research and production applications. Developed by Facebook’s AI Research lab, PyTorch offers a dynamic computational graph, making it a flexible choice for deep learning tasks. It provides an extensive collection of tools and libraries for building and training neural networks.

Key features:

  • Imperative programming style for intuitive model development
  • Dynamic computational graph enabling efficient debugging and experimentation
  • GPU acceleration for fast training and inference
  • Seamless integration with Python scientific computing libraries

0.0.3 Keras

Keras is a high-level deep learning API that acts as a front-end for various deep learning frameworks, including TensorFlow and PyTorch. It offers a user-friendly interface and abstracts away low-level details, making it easy to build and train deep learning models. Keras emphasizes simplicity, modularity, and extensibility.

Key features:

  • Simplified API for defining and training neural networks
  • Support for multi-backend, including TensorFlow and Theano
  • Built-in utilities for common deep learning tasks such as data preprocessing and model evaluation
  • Compatibility with Python scientific libraries and tools

0.0.4 Scikit-learn

Scikit-learn is a versatile and widely-used machine learning library in Python. It provides a rich set of algorithms and tools for tasks such as classification, regression, clustering, and dimensionality reduction. Scikit-learn is designed to be easy to use and allows users to leverage machine learning techniques efficiently.

Key features:

  • Comprehensive collection of supervised and unsupervised learning algorithms
  • Consistent API for training, testing, and deploying models
  • Robust preprocessing capabilities for data transformation and feature engineering
  • Extensive documentation and code examples for easy adoption

0.0.5 Pandas

Pandas is a powerful data manipulation and analysis library for Python. It provides easy-to-use data structures and data analysis tools, making it a go-to choice for data preprocessing and exploratory data analysis (EDA). Pandas excels at handling structured data and supports various data formats.

Key features:

  • Data structures like DataFrame and Series for efficient data handling
  • Rich set of functions for data cleaning, filtering, and transformation
  • Advanced indexing and slicing capabilities for data selection
  • Seamless integration with other Python libraries like NumPy and scikit-learn

0.0.6 Matplotlib

Matplotlib is a widely-used data visualization library in Python. It provides a flexible and comprehensive set of tools for creating static, animated, and interactive visualizations. Matplotlib is highly customizable, enabling users to create publication-quality plots for data exploration and presentation.

Key features:

  • Support for a wide range of plot types, including line plots, scatter plots, histograms, and more
  • Fine-grained control over plot aesthetics and customization options
  • Integration with Jupyter Notebook for interactive plotting
  • Extensive gallery of examples and tutorials for learning and inspiration

0.0.7 Seaborn

Seaborn is a statistical data visualization library built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. Seaborn complements Matplotlib by simplifying the process of creating complex visualizations and adding additional statistical capabilities.

Key features:

  • Specialized functions for creating informative statistical plots, such as distribution plots, regression plots, and categorical plots
  • Integration with Pandas data structures for easy data visualization and analysis
  • Customizable themes and color palettes for enhancing the aesthetics of plots
  • Built-in support for visualizing complex relationships and patterns in data

0.0.8 Scipy

Scipy is a powerful library for scientific and technical computing in Python. It provides a collection of modules for mathematical algorithms, optimization, integration, signal processing, statistics, and more. Scipy complements NumPy and provides additional functionality for scientific computations.

Key features:

  • Numerical routines for linear algebra, optimization, and interpolation
  • Integration and differential equation solvers for scientific simulations
  • Signal and image processing functions for working with digital signals and images
  • Statistical functions for probability distributions, hypothesis testing, and descriptive statistics