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Demystifying Probability and Statistics for Data Scientists with R
Paperback
General ComputersProbability & StatisticsProgramming
ISBN13: 9781032436210
Publisher: Auerbach Publications
Published: Jul 3 2026
Pages: 400
Language: English
Data science is a fast-emerging field of study and research. It mainly leverages integrated data analytics platforms, and in the recent past, the arrival of artificial intelligence (AI) technologies and tools to extract actionable insights out of burgeoning data volumes has totally changed the game of data science. Machine and deep learning (ML/DL) algorithms are the principal technologies of the AI paradigm. There are innovations and improvisations in the AI ecosystem that make sense out of a massive quantity of multi-structured data. The data science domain is acquiring special significance these days as businesses and governments across the globe need to fulfil the long-standing goal of data-driven insights and insight-driven decisions. Transitioning data into information and knowledge plays a pivotal role for worldwide enterprises and establishments in effectively initiating and implementing next-generation digital transformation projects.
Programming statistics and probability applications are key to this vital transformation of data into knowledge and insight. Probability and statistics are also key to decision-making processes that data science is automating. Demystifying Probability and Statistics for Data Scientists with R illustrates statistics with the R programming language, an emerging tool in data science. Filled with techniques for data science and analytics programming, this book is written for students and professionals. It is focused on learning outcomes and problem solving. Beginning with the basics of R programming, data science, and probability, the book progresses to methods, testing, and experiment design. Highlights of the book include:
- Types of data and collection methods
- Data visualization methods
- Probability basics
- Random variables and distributions
- Sampling methods and confidence intervals and hypothesis testing
- Design of experiments
- Correlation and regression
- Chi square test
- Non-parametric tests
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