Tamraparni Dasu – författare

Written for practitioners of data mining, data cleaning and database management.Presents a technical treatment of data quality including process, metrics, tools and algorithms.Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge.Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches.Uses case studies to illustrate applications in real life scenarios.Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.

Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.

Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.

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.