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{ "item_title" : "Topological Data Analysis", "item_author" : [" Simon Rudkin", "Jessica Nina Lester", "Mark Elliot "], "item_description" : "Taking data visualisation as a key tenet of empirical investigation, this book guides readers through the intuitive use of Topological Data Analysis Ball Mapper (TDABM). This emerging data science tool is central to model evaluation, research development and the visualisation of multivariate datasets. Despite the many benefits of data visualisation, empirical work often undervalues the data upon which statistical inferences are later drawn. Where there are visualisations, the ability to truly see what is happening within our data is constrained by the plots employed. TDABM offers a means to overcome these limitations to realise the benefits of visualisation.Taking an intuitive approach that shows how the necessary code can be constructed in R, examples throughout the book enable readers to build their analysis to suit whatever projects they are currently working on. This complete introduction to TDABM shows how it is informing and underpinning research in the social sciences and gives readers the confidence to apply it to their own data.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/35/056/438/1350564389_b.jpg", "price_data" : { "retail_price" : "100.00", "online_price" : "100.00", "our_price" : "100.00", "club_price" : "100.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Topological Data Analysis|Simon Rudkin

Topological Data Analysis : Research Methods

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Overview

Taking data visualisation as a key tenet of empirical investigation, this book guides readers through the intuitive use of Topological Data Analysis Ball Mapper (TDABM). This emerging data science tool is central to model evaluation, research development and the visualisation of multivariate datasets. Despite the many benefits of data visualisation, empirical work often undervalues the data upon which statistical inferences are later drawn. Where there are visualisations, the ability to truly see what is happening within our data is constrained by the plots employed. TDABM offers a means to overcome these limitations to realise the benefits of visualisation.
Taking an intuitive approach that shows how the necessary code can be constructed in R, examples throughout the book enable readers to build their analysis to suit whatever projects they are currently working on. This complete introduction to TDABM shows how it is informing and underpinning research in the social sciences and gives readers the confidence to apply it to their own data.

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Details

  • ISBN-13: 9781350564381
  • ISBN-10: 1350564389
  • Publisher: Bloomsbury Academic
  • Publish Date: January 2027
  • Dimensions: 9.21 x 6.14 x 1 inches
  • Shipping Weight: 1 pounds
  • Page Count: 256

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