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Veridical Data Science: An Actionable Framework for Real-World Applications
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
Veridical Data Science: An Actionable Framework for Real-World Applications
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
About this item
Veridical Data Science: An Actionable Framework for Real-World Applications
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
Veridical Data Science: An Actionable Framework for Real-World Applications
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
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Veridical Data Science by Bin Yu
See MorePrint Wave Designs
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About this item
Veridical Data Science: An Actionable Framework for Real-World Applications
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
Veridical Data Science: An Actionable Framework for Real-World Applications
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
About this item
Veridical Data Science: An Actionable Framework for Real-World Applications
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
Veridical Data Science: An Actionable Framework for Real-World Applications
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
In Veridical Data Science, Bin Yu and Rebecca Barter present a practical, real-world approach to data science, addressing the complexity of messy data and ambiguous questions. Unlike traditional textbooks, this work introduces the Predictability, Computability, and Stability (PCS) framework, which evaluates the trustworthiness of data-driven results by considering the uncertainties in data collection, cleaning, and modeling.
Featuring case studies, intuitive explanations, and R and Python code, this textbook is an accessible, self-contained guide for responsible data science, providing a solid foundation for advanced study.
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