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List of data science software

From Wikipedia, the free encyclopedia

This is a list of data science software and platforms used in data science, which includes programming languages, programming environments, machine learning frameworks, data engineering tools, statistical software, data analysis, plotting, MLOps systems, and more.

Programming languages

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Development environments

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These interactive notebooks, IDEs, and platforms provide specialised development environments.

Machine and deep learning software

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The Machine learning / deep learning tools support development in those fields.

Data engineering

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Examples of Data engineering tools.

Data mining

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Examples of Data mining tools.

Free and open-source

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Proprietary

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Database management

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Data warehouses

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Data warehouse environments include:

Data lakes

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Data lake environments include:

Algorithms

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Statistical software

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Open-source

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Public domain

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Freeware

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Proprietary

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Data processing

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Tools for Data processing and analysis:

Data and information visualization

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Software for Data visualization:

Plotting software

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Software for plotting data to support processing and visualise resuls.

Maps and geospatial visualization

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Machine learning

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MLOps and model deployment:

Data repositories

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See also

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Wikibooks has a book on the topic of: Data Science: An Introduction

References

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  1. ^ "Top 10 Java Libraries for Data Science". GeeksforGeeks. September 22, 2024.
  2. ^ "Swift for Data Science: An Introduction - Alibaba Cloud". www.alibabacloud.com.
  3. ^ "Top 12 Data Science Programming Languages | MDS@Rice". csweb.rice.edu.
  4. ^ "5 Types of Programming Languages for Data Scientists".
  5. ^ "The Role of Programming Languages in Data Science". New York Tech Online College of Engineering & Computer Sciences.
  6. ^ "Apache Zeppelin 0.10.0 Documentation". zeppelin.apache.org.
  7. ^ Monaco, Michael A.; Dexter, Marie; Tamburro, Jennifer. "Introduction to SAS® Studio" (PDF). Proceedings 2014 Paper SAS302-2014. Cary, NC: SAS Institute Inc.
  8. ^ "6 Best Python IDEs for Data Science in 2025". www.datacamp.com.
  9. ^ "8 Best Machine Learning Software To Use in 2025".
  10. ^ Hiter, Shelby (April 25, 2023). "10 Best Data Mining Tools & Software".
  11. ^ "Cloud Data Warehouse Comparison: Amazon Redshift, Google BigQuery, Azure Synapse, Snowflake, and Databricks". www.linkedin.com.
  12. ^ Darley, James (September 24, 2025). "Top 10: AI Data Lakes". aimagazine.com.
  13. ^ "Top 10 Algorithms for Data Science".
  14. ^ "Machine Learning Algorithms". 17 August 2023.
  15. ^ Staff, Coursera (May 9, 2025). "15 Data Analysis Tools and When to Use Them". Coursera.
  16. ^ "BentoML". GitHub.
  17. ^ "MLflow". mlflow.org.
  18. ^ Zaharia, Matei A.; Chen, Andrew; Davidson, Aaron; Ghodsi, Ali; Hong, Sue Ann; Konwinski, Andy; Murching, Siddharth; Nykodym, Tomas; Ogilvie, Paul; Parkhe, Mani; Xie, Fen; Zumar, Corey (September 28, 2018). "Accelerating the Machine Learning Lifecycle with MLflow". IEEE Data Eng. Bull. – via GitHub.
  19. ^ "Production-ready ML Serving Framework | Seldon Core 2". docs.seldon.ai.
  20. ^ "Streamlit/Streamlit". GitHub .
  21. ^ https://docs.streamlit.io
  22. ^ "Serving Models | TFX". TensorFlow.
  23. ^ "tensorflow/serving". September 27, 2025 – via GitHub.
  24. ^ "wandb/wandb". September 28, 2025 – via GitHub.
  25. ^ "Find Open Datasets and Machine Learning Projects | Kaggle".
  26. ^ https://archive.ics.uci.edu
  27. ^ "OpenML".
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