Foundations Of Statistics For Data Scientists With R And Python(1st Edition)
Authors:
Alan Agresti
Type:Hardcover/ PaperBack / Loose Leaf
Condition: Used/New
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Book details
ISBN: 0367748452, 978-0367748456
Book publisher: Chapman And Hall/CRC
Book Price $63.02 : Alan Agresti's 'Foundations Of Statistics For Data Scientists With R And Python' is a seminal work providing a comprehensive introduction to statistical methodologies crucial for data science. This book stands out due to its duality in using both R and Python, two of the most popular programming languages in data analytics. Without adhering to complex mathematical formality, Agresti effectively presents statistical concepts like probability, regression analysis, and hypothesis testing, making them accessible to both novices and seasoned professionals. The book is structured to accommodate learners with detailed solution manuals and an answer key that provide clarity and make the learning process interactive and fruitful. The table of contents is meticulously designed to guide readers through topics, starting from basic statistical principles to advanced predictive modeling and machine learning techniques. This work is particularly beneficial for those involved in data science, as it harmonizes theory with practical application. The reception among academia and industry professionals has been notably positive, highlighting its utility as both a textbook and a professional guide. Readers appreciate the balance between technical rigor and accessibility, making it a go-to resource for mastering statistical tools crucial in data science roles. Its emphasis on R and Python ensures that readers gain proficiency in two languages essential for data analysis and data science careers. The product serves as a cheap and reliable alternative to pricier academic help and tutoring services.
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RH
Hands down the best resource I've found for learning statistics tailored to data science. This book simplifies complex concepts with clear explanations and includes practical examples in both R and Python. It arrived in pristine condition, well-packaged, and earlier than expected. Kudos to the author for blending statistical theory with practical skills, making it a must-have for anyone keen on data science!































