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Chapman & Hall/CRC data science series

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Chapman & Hall/CRC data science series

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CRC Press
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Paper
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Table of Contents

  • Basketball data science : with applications in R

  • A tour of data science : learn R and Python in parallel

  • Data science : a first introduction

  • Data science : a first introduction

  • JavaScript for data science

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Paper

Material Type
図書
Publication, Distribution, etc.
Alternative Title
Data science series
Place of Publication (Country Code)
us
Target Audience
一般
Related Material
Basketball data science : with applications in R
A tour of data science : learn R and Python in parallel
Data science : a first introduction
Data science : a first introduction
JavaScript for data science
Practitioner's guide to data science
Data science : a first introduction
Introduction to environmental data science
Telling stories with data : with applications in R
Geographic data science with R : visualizing and analyzing environmental change
Public policy analytics : code and context for data science in government
Feature engineering and selection : a practical approach for predictive models
Probability and statistics for data science : Math+R+Data
Public policy analytics : code and context for data science in government
Tree-based methods for statisitcal learning in R
Cybersecurity analytics
Supervised machine learning for text analysis in R
Supervised machine learning for text analysis in R
Introduction to data science : data analysis and prediction algorithms with R
Supervised machine learning for text analysis in R
Massive graph analytics
Urban informatics : using big data to understand and serve communities
Hands-on data science for librarians
Introduction to NFL analytics with R
Big data analytics : a guide to data science practitioners making the transition to big data
Explanatory model analysis : explore, explain, and examine predictive models
Data analytics : a small data approach
A tour of data science : learn R and Python in parallel
How to think about data science
Big data analytics : a guide to data science practitioners making the transition to big data
Spatial statistics for data science : theory and practice with R
Statistical foundation of data science
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