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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
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Über die Autorin bzw. den Autor
Peter Bruce is the Founder of the Institute for Statistics Education at Statistics.com, which offers about 80 courses in statistics and analytics, roughly half of which are aimed at data scientists. He has authored or co-authored several books in statistics and analytics, and he earned his Bachelor's degree at Prin
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Paperback. Zustand: new. Paperback. 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 outcomesApply sampling and experimental design to reduce bias and answer questions with clarityUse regression to understand data-generating processes and detect anomaliesBuild predictive models using classification, clustering, and unsupervised learning with unbalanced data 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 and Gemini work, and how they can support real-world statistical workflows. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798341666283
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Paperback. Zustand: New. 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 outcomesApply sampling and experimental design to reduce bias and answer questions with clarityUse regression to understand data-generating processes and detect anomaliesBuild predictive models using classification, clustering, and unsupervised learning with unbalanced data. Bestandsnummer des Verkäufers LU-9798341666283
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Paperback. Zustand: New. 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 outcomesApply sampling and experimental design to reduce bias and answer questions with clarityUse regression to understand data-generating processes and detect anomaliesBuild predictive models using classification, clustering, and unsupervised learning with unbalanced data. Bestandsnummer des Verkäufers LU-9798341666283
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Paperback. Zustand: new. Paperback. 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 outcomesApply sampling and experimental design to reduce bias and answer questions with clarityUse regression to understand data-generating processes and detect anomaliesBuild predictive models using classification, clustering, and unsupervised learning with unbalanced data 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 and Gemini work, and how they can support real-world statistical workflows. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798341666283
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Taschenbuch. Zustand: Neu. Neuware -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 400 pp. Englisch. Bestandsnummer des Verkäufers 9798341666283
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Taschenbuch. Zustand: Neu. Neuware -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 400 pp. Englisch. Bestandsnummer des Verkäufers 9798341666283
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