Popular Data Science Packages
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.
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
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
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
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
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
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
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
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