IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-319-68837-4_29.html
   My bibliography  Save this book chapter

Introduction to Python

In: Essentials of Business Analytics

Author

Listed:
  • Peeyush Taori

    (London Business School)

  • Hemanth Kumar Dasararaju

    (Indian School of Business)

Abstract

As data science is increasingly being adopted in the industry, the demand for data scientists is also growing at an astonishing pace. Data scientists are a rare breed of “unicorns” who are required to be omniscient and according to popular culture, a data scientist is someone who knows more statistics than a programmer and more programming than a statistician. One of the most important tools in a data scientist’s toolkit is the knowledge of a general-purpose programming language that enables a data scientist to perform tasks of data cleaning, data manipulation, and statistical analysis with ease. Such requirements call for programming languages that are easy enough to learn and yet powerful enough to accomplish complex coding tasks. Two such de facto programming languages for data science used in industry and academia are Python and R.

Suggested Citation

  • Peeyush Taori & Hemanth Kumar Dasararaju, 2019. "Introduction to Python," International Series in Operations Research & Management Science, in: Bhimasankaram Pochiraju & Sridhar Seshadri (ed.), Essentials of Business Analytics, chapter 0, pages 917-944, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-68837-4_29
    DOI: 10.1007/978-3-319-68837-4_29
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:isochp:978-3-319-68837-4_29. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.