Statistics and Data Foundations for AI is an interdisciplinary approach to statistical concepts and data foundations of AI with real-world illustrative examples from authoritative sources such as NASA, NOAA and the United States Census Bureau. Co-authored by a data science research expert and an experienced educator, the book serves as a prequel to an AI and machine learning course.Given the interdependence of data and AI, understanding data and using it responsibly to create and interact with AI tools requires a high level of statistical skill and data intuition. The book includes topics such as data management, exploratory data analysis, sampling, probability theory, hypothesis testing, multivariate analysis, data quality, ethics, data privacy, and responsible use of AI. Every key statistical concept is presented in the context of how it is used by AI applications in areas such as sports, fashion, climate science, environmental science, health, medicine, and space exploration. The book makes AI relatable to everyday life so that it is no longer an abstraction. Instructor resources, supplementary materials, further reading, and debate topics enable advanced study and deeper thinking.Statistics and Data Foundations for AI is intended for undergraduate and graduate students, and practitioners interested in learning statistical foundations in relation to data and AI with application to real-world problems. The content is accessible to learners from a wide variety of backgrounds (STEM and non-STEM) without sacrificing rigor.

Statistics and Data Foundations for AI is an interdisciplinary approach to statistical concepts and data foundations of AI with real-world illustrative examples from authoritative sources such as NASA, NOAA and the United States Census Bureau. Co-authored by a data science research expert and an experienced educator, the book serves as a prequel to an AI and machine learning course.Given the interdependence of data and AI, understanding data and using it responsibly to create and interact with AI tools requires a high level of statistical skill and data intuition. The book includes topics such as data management, exploratory data analysis, sampling, probability theory, hypothesis testing, multivariate analysis, data quality, ethics, data privacy, and responsible use of AI. Every key statistical concept is presented in the context of how it is used by AI applications in areas such as sports, fashion, climate science, environmental science, health, medicine, and space exploration. The book makes AI relatable to everyday life so that it is no longer an abstraction. Instructor resources, supplementary materials, further reading, and debate topics enable advanced study and deeper thinking.Statistics and Data Foundations for AI is intended for undergraduate and graduate students, and practitioners interested in learning statistical foundations in relation to data and AI with application to real-world problems. The content is accessible to learners from a wide variety of backgrounds (STEM and non-STEM) without sacrificing rigor.

Statistics and Data Foundations for AI is an interdisciplinary approach to statistical concepts and data foundations of AI with real-world illustrative examples from authoritative sources such as NASA, NOAA and the United States Census Bureau. Co-authored by a data science research expert and an experienced educator, the book serves as a prequel to an AI and machine learning course. Given the interdependence of data and AI, understanding data and using it responsibly to create and interact with AI tools requires a high level of statistical skill and data intuition. The book includes topics such as data management, exploratory data analysis, sampling, probability theory, hypothesis testing, multivariate analysis, data quality, ethics, data privacy, and responsible use of AI. Every key statistical concept is presented in the context of how it is used by AI applications in areas such as sports, fashion, climate science, environmental science, health, medicine, and space exploration. The book makes AI relatable to everyday life so that it is no longer an abstraction. Instructor resources, supplementary materials, further reading, and debate topics enable advanced study and deeper thinking. Statistics and Data Foundations for AI is intended for undergraduate and graduate students, and practitioners interested in learning statistical foundations in relation to data and AI with application to real-world problems. The content is accessible to learners from a wide variety of backgrounds (STEM and non-STEM) without sacrificing rigor.

Statistics and Data Foundations for AI is an interdisciplinary approach to statistical concepts and data foundations of AI with real-world illustrative examples from authoritative sources such as NASA, NOAA and the United States Census Bureau. Co-authored by a data science research expert and an experienced educator, the book serves as a prequel to an AI and machine learning course.Given the interdependence of data and AI, understanding data and using it responsibly to create and interact with AI tools requires a high level of statistical skill and data intuition. The book includes topics such as data management, exploratory data analysis, sampling, probability theory, hypothesis testing, multivariate analysis, data quality, ethics, data privacy, and responsible use of AI. Every key statistical concept is presented in the context of how it is used by AI applications in areas such as sports, fashion, climate science, environmental science, health, medicine, and space exploration. The book makes AI relatable to everyday life so that it is no longer an abstraction. Instructor resources, supplementary materials, further reading, and debate topics enable advanced study and deeper thinking.Statistics and Data Foundations for AI is intended for undergraduate and graduate students, and practitioners interested in learning statistical foundations in relation to data and AI with application to real-world problems. The content is accessible to learners from a wide variety of backgrounds (STEM and non-STEM) without sacrificing rigor.

Statistics and Data Foundations for AI is an interdisciplinary approach to statistical concepts and data foundations of AI with real-world illustrative examples from authoritative sources such as NASA, NOAA and the United States Census Bureau. Co-authored by a data science research expert and an experienced educator, the book serves as a prequel to an AI and machine learning course.Given the interdependence of data and AI, understanding data and using it responsibly to create and interact with AI tools requires a high level of statistical skill and data intuition. The book includes topics such as data management, exploratory data analysis, sampling, probability theory, hypothesis testing, multivariate analysis, data quality, ethics, data privacy, and responsible use of AI. Every key statistical concept is presented in the context of how it is used by AI applications in areas such as sports, fashion, climate science, environmental science, health, medicine, and space exploration. The book makes AI relatable to everyday life so that it is no longer an abstraction. Instructor resources, supplementary materials, further reading, and debate topics enable advanced study and deeper thinking.Statistics and Data Foundations for AI is intended for undergraduate and graduate students, and practitioners interested in learning statistical foundations in relation to data and AI with application to real-world problems. The content is accessible to learners from a wide variety of backgrounds (STEM and non-STEM) without sacrificing rigor.

Statistics and Data Foundations for AI is an interdisciplinary approach to statistical concepts and data foundations of AI with real-world illustrative examples from authoritative sources such as NASA, NOAA and the United States Census Bureau. Co-authored by a data science research expert and an experienced educator, the book serves as a prequel to an AI and machine learning course. Given the interdependence of data and AI, understanding data and using it responsibly to create and interact with AI tools requires a high level of statistical skill and data intuition. The book includes topics such as data management, exploratory data analysis, sampling, probability theory, hypothesis testing, multivariate analysis, data quality, ethics, data privacy, and responsible use of AI. Every key statistical concept is presented in the context of how it is used by AI applications in areas such as sports, fashion, climate science, environmental science, health, medicine, and space exploration. The book makes AI relatable to everyday life so that it is no longer an abstraction. Instructor resources, supplementary materials, further reading, and debate topics enable advanced study and deeper thinking. Statistics and Data Foundations for AI is intended for undergraduate and graduate students, and practitioners interested in learning statistical foundations in relation to data and AI with application to real-world problems. The content is accessible to learners from a wide variety of backgrounds (STEM and non-STEM) without sacrificing rigor.