Description

Book Synopsis
Data Science Fundamentals with R, Python, and Open Data

Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projects

Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate.

This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks

Data Science Fundamentals with R Python and Open

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    A Hardback by Marco Cremonini

    £93.60

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    RRP £104.00 – you save £10.40 (10%)

    Order before 4pm today for delivery by Tue 17 Nov 2026.

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      Book details

      Publisher John Wiley & Sons Inc
      Published 3/22/2024
      ISBN-13 9781394213245
      978-1394213245
      ISBN-10 1394213247
      Also in:
      Mathematics

      Description

      Book Synopsis
      Data Science Fundamentals with R, Python, and Open Data

      Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projects

      Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate.

      This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks

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