Practical Statistics for Data Scientists
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Practical Statistics for Data Scientists
50+ Essential Concepts Using R and Python
$39.90
$67.90
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Detailed Overview:
Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce, and Peter Gedeck is a highly regarded guide that bridges the gap between traditional statistics and modern data science applications. Designed specifically for data scientists, analysts, programmers, and machine learning practitioners, the book focuses on practical statistical concepts used in real-world data analysis and predictive modeling.
Rather than emphasizing complex mathematical theory, the book explains statistical techniques through hands-on examples, intuitive explanations, and practical use cases relevant to data science workflows. Readers gain a strong understanding of probability, regression, classification, sampling, experimental design, statistical inference, and machine learning fundamentals.
The book also demonstrates how statistical thinking supports data-driven decision-making and improves model performance. Using examples in Python and R, it helps readers apply statistical methods directly to modern analytics and machine learning tasks.
Widely used by students, professionals, and aspiring data scientists, Practical Statistics for Data Scientists is an essential resource for anyone seeking to strengthen analytical skills and build a solid foundation in applied statistics for data science and artificial intelligence projects.
Product Details:
Practical Statistics for Data Scientists introduces essential statistical methods and concepts used in data science, machine learning, and analytics. The book focuses on practical applications rather than heavy mathematical theory, making complex statistical topics easier to understand and apply.
Readers learn about data distributions, probability, hypothesis testing, regression analysis, classification, resampling methods, experimental design, and predictive modeling techniques. The authors also explain how statistical reasoning improves data analysis and supports better business and research decisions.
The book includes practical examples using Python and R, helping readers apply concepts directly to real-world data science projects. It is ideal for data scientists, analysts, students, programmers, and professionals looking to strengthen their understanding of applied statistics in modern data-driven environments.
Peter Bruce is a statistician, educator, and co-founder of the Institute for Statistics Education. He is widely recognized for making statistical concepts accessible to professionals in technology and analytics.
Andrew Bruce and Peter Gedeck are experienced data science professionals and educators known for their contributions to analytics, machine learning, and applied statistics education. Together, they have created one of the most respected practical guides for modern data science learners and professionals.
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