
Statistical Fundamentals for Data Science Applicaions (English Version)
Book Details
- Author: Amiya Ranjan Bhowmick
- Language: English
- Pages: 383
- Format: Paperback
- ISBN: 9789389314717
- Publisher: Techno World
About the Book
Statistical Fundamentals for Data Science Applications is an insightful and practical guide that bridges the gap between classical statistics and modern data science applications. Written by Dr. Amiya Ranjan Bhowmick, this book presents statistical concepts through an engaging blend of theoretical understanding and simulation-based learning. Rather than relying solely on formulas and shortcuts, the author emphasizes conceptual clarity and real-world reasoning, enabling readers to understand how statistical methods work and why they matter in practical data analysis.
The book explores important topics such as probability, statistical inference, Central Limit Theorem, maximum likelihood estimation, hypothesis testing, convergence, and uncertainty analysis through carefully designed computational examples and simulations. Its approachable style makes complex statistical ideas easier to grasp for students, educators, researchers, and professionals interested in data science and analytics.
In today’s rapidly growing field of data science, where many resources focus only on software tools, this book stands out by providing a strong foundation in statistical reasoning and principled methodology. It serves as an excellent academic and professional resource for readers seeking a deeper understanding of statistics as the backbone of data-driven decision-making.
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Book Details
- Author: Amiya Ranjan Bhowmick
- Language: English
- Pages: 383
- Format: Paperback
- ISBN: 9789389314717
- Publisher: Techno World
About the Book
Statistical Fundamentals for Data Science Applications is an insightful and practical guide that bridges the gap between classical statistics and modern data science applications. Written by Dr. Amiya Ranjan Bhowmick, this book presents statistical concepts through an engaging blend of theoretical understanding and simulation-based learning. Rather than relying solely on formulas and shortcuts, the author emphasizes conceptual clarity and real-world reasoning, enabling readers to understand how statistical methods work and why they matter in practical data analysis.
The book explores important topics such as probability, statistical inference, Central Limit Theorem, maximum likelihood estimation, hypothesis testing, convergence, and uncertainty analysis through carefully designed computational examples and simulations. Its approachable style makes complex statistical ideas easier to grasp for students, educators, researchers, and professionals interested in data science and analytics.
In today’s rapidly growing field of data science, where many resources focus only on software tools, this book stands out by providing a strong foundation in statistical reasoning and principled methodology. It serves as an excellent academic and professional resource for readers seeking a deeper understanding of statistics as the backbone of data-driven decision-making.














