Best Data Science Frameworks

By GUVI Network


– Open-source ML library developed by Google – Can handle large-scale numerical computation & data flow graphs – Flexibility and scalability in building deep learning model – Integration with various data sources


– Simple, versatile, and extensive documentation – Flexibility and scalability in building deep learning model – Integration with various data sources – Suitable for both beginners and experienced data scientists


– Open-source neural network library written in Python – Can run on top of TensorFlow, Theano, and CNTK – User-friendly, simple, beginner-friendly, & easy-to-implement – Suitable for building complex deep learning architectures


– Open-source library for data manipulation and analysis in Python – Offers data structures and complex operations for structured data – Provides tools for data cleaning, reshaping, merging, and slicing – Well-suited for handling both small and large datasets

Spark MLib

– Machine learning library built on top of Apache Spark – Supports Java, Scala, Python, and R programming languages – Enables distributed data processing and parallel computation – Ideal for handling big data and complex ML tasks


– Open-source ML library developed by Facebook’s AI research group – Emphasizes flexibility and dynamic computation – Widely used for deep learning applications – Supports dynamic neural networks and allows real-time changes to the architecture


– Popular plotting data visualization library for Python – Provides a wide range of visualization options for data exploration – Supports various types of plots, including histograms, scatterplots, and 3D plots – Integrates well with other Python libraries like NumPy and Pandas


– Key Python library for scientific computing & analysis of large datasets – Offers efficient data structures for multi-dimensional arrays – Supports mathematical operations and integration with other languages like C/C++ – Seamless integration with other data science libraries


– Python data visualization library based on Matplotlib – Creates aesthetically pleasing statistical visualizations for complex relations – Offers advanced features for visualizing distributions and relationships – Intuitive interface for creating compelling visualizations


– Python library for efficient numerical computation – Well-suited for deep learning applications – Supports both CPU and GPU computations – Provides automatic differentiation for building complex models

Read the detailed blog to learn more about the top data science frameworks in 2023.