SpringerLink
Log in

Data Science Tools

  • Chapter
  • First Online:
Introduction to Data Science

Part of the book series: Undergraduate Topics in Computer Science ((UTICS))

  • 730 Accesses

Abstract

In this chapter, first we introduce some of the cornerstone tools that data scientists use. The toolbox of any data scientist, as for any kind of programmer, is an essential ingredient for success and enhanced performance. Choosing the right tools can save a lot of time and thereby allow us to focus on data analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.python.org/downloads/.

  2. 2.

    http://ipython.org/install.html.

  3. 3.

    https://github.com/features/copilot.

  4. 4.

    http://www.scipy.org/scipylib/download.html.

  5. 5.

    http://www.scipy.org/scipylib/download.html.

  6. 6.

    https://www.tensorflow.org/.

  7. 7.

    https://keras.io/.

  8. 8.

    https://pytorch.org/.

  9. 9.

    http://pandas.pydata.org/getpandas.html.

  10. 10.

    http://continuum.io/downloads.

  11. 11.

    https://netbeans.org/downloads/.

  12. 12.

    https://eclipse.org/downloads/.

  13. 13.

    https://code.visualstudio.com/.

  14. 14.

    https://www.jetbrains.com/pycharm/.

  15. 15.

    https://wingware.com/.

  16. 16.

    https://github.com/spyder-ide/spyder.

  17. 17.

    Eric https://eric-ide.python-projects.org/.

  18. 18.

    http://jupyter.readthedocs.org/en/latest/install.html.

  19. 19.

    https://colab.research.google.com/.

  20. 20.

    https://zenodo.org/.

  21. 21.

    https://ec.europa.eu/eurostat.

  22. 22.

    https://archive.ics.uci.edu/.

  23. 23.

    https://git-scm.com/.

  24. 24.

    https://github.com/.

  25. 25.

    https://dvc.org/.

  26. 26.

    https://pandas.pydata.org/docs/.

Acknowledgements

This chapter was writen by Eloi Puertas and Francesc Dantí.

Author information

Authors and Affiliations

  1. Dept. of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes, 585, 08007, Barcelona, Spain

    Eloi Puertas

Authors
  1. Eloi Puertas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eloi Puertas .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Puertas, E. (2024). Data Science Tools. In: Introduction to Data Science. Undergraduate Topics in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-031-48956-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48956-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48955-6

  • Online ISBN: 978-3-031-48956-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation