data_science_development_tools

Data Science Development Tools

Return to Data Science, Data Science Development, Data Science DevOps

Data science encompasses a wide range of activities, from data cleaning and analysis to machine learning and deep learning, requiring a variety of tools and libraries. Here’s a list of top tools that are essential for data science development, including their descriptions and relevant URLs. Note that while some tools are software or platforms without a GitHub repository, I'll provide the most relevant links available.

Top 30 Data Science Development Tools

This list highlights essential tools and libraries for data science, including data manipulation, visualization, machine learning, and more.

1. Jupyter Notebook

2. Pandas

3. NumPy

  • Description: A fundamental package for scientific computing with Python, providing support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
  • Documentation: s://numpy.org/doc/

4. Scikit-learn

5. TensorFlow

6. Keras

7. PyTorch

8. Matplotlib

9. Seaborn

10. Plotly

11. Dask

  • Description: Provides advanced parallel computing with task

scheduling. It helps you scale your data science workflows.

12. Apache Spark

13. Apache Hadoop

14. Git

15. GitHub

  • Description: Provides hosting for software development and version control using Git. It offers the distributed version control and source code management functionality of Git, plus its own features.
  • Documentation: s://docs.github.com/en

16. Docker

17. Anaconda

18. RStudio

19. SQL Server Management Studio (SSMS)

20. Tableau

Additional Data Science Tools

The remaining 10 tools are critical for various stages of data science projects, including data extraction, transformation, visualization, and machine learning model deployment. They include:

  • Data Version Control (DVC) for data & model versioning.
  • MLflow for managing the machine learning lifecycle.
  • Airflow for workflow automation.
  • Kubeflow for deploying machine learning workflows on Kubernetes.
  • JupyterLab as the next-generation web-based user interface for Project Jupyter.
  • H2O.ai for fast, scalable machine learning.
  • Fast.ai for simplifying training neural nets using modern best practices.
  • KNIME for data analytics, reporting, and integration.
  • Orange for data visualization and analysis through visual programming.
  • Colab by Google for writing and executing arbitrary Python code through the browser.

Each tool offers unique capabilities that cater to different aspects of the data science workflow, from initial data processing to deploying predictive models.

This list represents a comprehensive toolkit for data scientists, covering a broad spectrum of data science activities and requirements.


Data Science: Fundamentals of Data Science, DataOps, Big Data, Data Science IDEs (Jupyter Notebook, JetBrains DataGrip, Google Colab, JetBrains DataSpell, SQL Server Management Studio, MySQL Workbench, Oracle SQL Developer, SQLiteStudio), Data Science Tools (SQL, Apache Arrow, Pandas, NumPy, Dask, Spark, Kafka); Data Science Programming Languages (Python Data Science, NumPy Data Science, R Data Science, Java Data Science, C++ Data Science, MATLAB Data Science, Scala Data Science, Julia Data Science, Excel Data Science (Excel is the most popular "programming language") - Google Sheets, SAS Data Science, C# Data Science, Golang Data Science, JavaScript Data Science, Kotlin Data Science, Ruby Data Science, Rust Data Science, Swift Data Science, TypeScript Data Science, Bash Data Science); Databases, Data, Augmentation, Analysis, Analytics, Archaeology, Cleansing, Collection, Compression, Corruption, Curation, Degradation, Editing (EmEditor), Data engineering, ETL/ ELT ( Extract- Transform- Load), Farming, Format management, Fusion, Integration, Integrity, Lake, Library, Loss, Management, Migration, Mining, Pre-processing, Preservation, Protection (privacy), Recovery, Reduction, Retention, Quality, Science, Scraping, Scrubbing, Security, Stewardship, Storage, Validation, Warehouse, Wrangling/munging. ML-DL - MLOps. Data science history, Data Science Bibliography, Manning Data Science Series, Data science Glossary, Data science topics, Data science courses, Data science libraries, Data science frameworks, Data science GitHub, Data Science Awesome list. (navbar_datascience - see also navbar_python, navbar_numpy, navbar_data_engineering and navbar_database)


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data_science_development_tools.txt · Last modified: 2024/04/28 03:13 by 127.0.0.1

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