Statistics and Data Foundations for AI

1. Auflage 2026
ISBN: 978-1-041-00642-8
Verlag: Taylor & Francis

Buch, Englisch, 270 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 660 g

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.

Zielgruppe

Academic, Professional Practice & Development, and Undergraduate Core

Autoren/Hrsg.

Dasu, Tamraparni

Murthy, Geetha

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

1. Statistics, Data and AI 2. About Data 3. Exploratory Data Analysis 4. Sampling: Less is More 5. Probability in the Age of AI 6. Data-driven Hypotheses Testing 7. Variable Relationships: The Full
Picture 8. Data Quality and AI 9. What’s that AI 10. Responsible Use of Data and AI

Über Autor(innen)

Dr. Tamraparni Dasu (Ph.D. Mathematical Statistics, University of Rochester, 1991) is a research scientist and Data Science expert specializing in computational statistics, machine learning and data quality. She retired as Lead Inventive Scientist after 31 years at AT&T Bell Laboratories and now teaches Data Mining, Machine Learning and AI as an adjunct professor at Fairleigh Dickinson University, New Jersey. Dr. Dasu has published extensively in top-tier journals and research conferences such as SIGMOD, KDD and VLDB and authored the field’s first technical book on data quality, Exploratory Data Mining and Data Cleaning, John Wiley (2003), with Dr. Theodore Johnson. As an educator, Dr. Dasu is committed to mentoring the next generation of quantitative thinkers, computer scientists and data scientists.

Dr. Geetha Murthy (Ed.D. Instructional Leadership, St. John’s University, Queens, New York, 2015) is a highly experienced educator and administrator whose research focused on describing and dismantling self-limiting beliefs in students that inhibit them from pursuing STEM education/careers. Most recently, Dr. Murthy served as the K-12 director of mathematics for Herricks School District in Long Island, NY, a high-performing public school district. Under her leadership, the district made significant progress in increasing student achievement through initiatives that focused on systemic changes to create and sustain greater equity, access and success for all students. Dr. Murthy is passionate about promoting 21st Century Skills in teaching and learning.

The authors believe that statistics and data foundations are essential in an increasingly AI-native world and should be accessible to students and practitioners from varied backgrounds. The authors have a unique combination of complementary skills, a deep understanding of the core set of fundamental concepts and practice of statistics and data science, and extensive experience and insights into foundational education, that makes this a canonical book for the study of statistics and data in relation to AI.