Foundations of Data Analysis
Mike Nguyen
set 2025 · Springer Nature
Ebook
287
pagine
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Informazioni su questo ebook
This volume is designed not only as a foundation for mastering modern data analysis but also as a trusted companion for revision and review. Whether you're encountering these ideas for the first time or revisiting them to reinforce your understanding, this book provides a clear and structured pathway.
It introduces essential topics such as descriptive statistics, probability, and statistical inference, while laying the groundwork for more advanced analytical methods. Emphasis is placed on conceptual clarity, interpretation, and practical intuition—making the material both rigorous and accessible.
Rich with examples from business, economics, and policy research, this book equips students, professionals, and self-learners alike with the tools needed for applied data analysis and future modeling work.
It introduces essential topics such as descriptive statistics, probability, and statistical inference, while laying the groundwork for more advanced analytical methods. Emphasis is placed on conceptual clarity, interpretation, and practical intuition—making the material both rigorous and accessible.
Rich with examples from business, economics, and policy research, this book equips students, professionals, and self-learners alike with the tools needed for applied data analysis and future modeling work.
Informazioni sull'autore
Dr. Mike Nguyen is a professor of marketing and data science whose research is deeply informed by active collaborations with firms in telecommunications, financial institutions, and digital platforms. He teaches data analytics and causal inference at both undergraduate and graduate levels, with an emphasis on business applications in marketing, finance, and strategic decision-making. His work integrates econometrics, machine learning, and experimental design to tackle real-world problems. Dr. Nguyen designs research-based solutions in partnership with industry to inform both theory and practice.
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