OReilly.Media.Inc.-.AI-Assisted.Statistics.For.Data.Scientists.3rd.Edition.2026.RETAIL.EPUB.eBook-CTO

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eBooks/Audiobooks
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CTO
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9,13 MB
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Date
2026-06-26

NFO

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                              O'Reilly Media, Inc
                                  Peter Bruce
             AI-Assisted Statistics For Data Scientists 3rd Edition
                                      N/A
                                      2026
        _.--.__.-'""'-.__.--.__.-'""'-.__.--.__.-'""'-.__.--.__.-'""'-._
      Publisher > O'Reilly Media, Inc
      Author    > Peter Bruce
      Title     > AI-Assisted Statistics For Data Scientists 3rd Edition
      Issue     > N/A
      Year      > 2026
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      ISBN      > 9798341666283
      Pages     > 700
      Genre     > Statistics
      Language  > English
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      Type      > RETAIL
      Media     > TECH
      Format    > .epub
      Section   > eBook
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      Published > 2026-06
      Release   > 2026-06-26
      Disks     > 02 x 5 MB
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                 -  - --   [ D E S C R I P T I O N ]   -- -  -
  Statistical methods are a key part of data science, yet few data scientists  
    have formal statistical training. Courses and books on basic statistics    
   rarely cover the topic from a data science perspective. The third edition   
    of this popular guide expands its practical foundations in R and Python    
     into the modern AI toolkit, with new chapters on neural networks, deep    
        learning, and large language models. Generative AI is integrated       
  throughout, showing how tools such as ChatGPT, Claude, and Gemini work, and  
  how they can support real-world statistical workflows. This book highlights  
     concepts that matter most when working with data, building predictive     
     models, and deploying AI responsibly. If you're comfortable with R or     
      Python and have had some exposure to basic statistics, this concise      
   reference will boost your statistical literacy, your understanding of how   
   AI works, and your confidence in real-world data science and AI projects.   
   Conduct exploratory analysis of data to improve quality and model outcomes  
   Apply sampling and experimental design to reduce bias and answer questions  
    with clarity Use regression to understand data-generating processes and    
   detect anomalies Build predictive models using classification, clustering,  
                 and unsupervised learning with unbalanced data                
                  -  - --   [ R E T A I L   U R L ]   -- -  -                  
     https://www.ebooks.com/book/347495910/ai-assisted-statistics-for-data-    
                            scientists/peter-bruce/                            
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