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Probability and Statistics for Computer Science
R$ 532
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Probability and Statistics for Computer Science provides a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.
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Detalhes do produto
| Item Weight | 1.5 lbs (680 grams) |
Para quem é indicado?
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Computer Science Students
Ideal for students studying computer science nuances in probability and statistics for algorithmic and data analysis.
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Data Analysts
Great resource for data analysts looking to strengthen their statistical foundation and understanding for data interpretation.
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Machine Learning Enthusiasts
Highly beneficial for those entering machine learning, providing insights into probability models and statistical methodologies.
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Complete Beginners
Individuals with no prior knowledge of statistics may struggle with the concepts without foundational background.
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Advanced Statisticians
Experienced statisticians may find the content basic and lacking depth for advanced statistical theories and applications.
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Casual Learners
Not suitable for those seeking a light introduction; it’s focused and requires commitment to comprehend effectively.
DESCRIÇÃO DO PRODUTO
Probability and Statistics for Computer Science
Perguntas e Respostas do cliente
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pergunta:
What topics are covered in Probability and Statistics for Computer Science?
responda: This book provides an extensive overview of key concepts such as probability theory, statistical inference, hypothesis testing, and regression analysis. It critically examines the mathematical foundations necessary for data analysis and offers practical applications relevant to computer science. Students and professionals will find it beneficial for understanding machine learning algorithms and data-driven decision making. -
pergunta:
Who is the target audience for this book?
responda: The book is primarily aimed at computer science students and professionals who wish to enhance their understanding of probability and statistics. It’s also suitable for researchers looking to apply statistical methods in their work. With its clear explanations and practical examples, it serves as an excellent resource for anyone interested in data science, machine learning, or quantitative analysis. -
pergunta:
Is the book suitable for beginners in statistics and probability?
responda: Yes, Probability and Statistics for Computer Science is designed to be accessible to beginners. The author begins with fundamental concepts, progressively building up to more complex topics. Clear definitions, examples, and exercises help facilitate a smooth learning curve, making it ideal for novices who are eager to learn data analysis principles. -
pergunta:
Does the book include practical exercises or examples?
responda: Absolutely! The book is replete with practical exercises and real-world examples. These not only reinforce the theoretical concepts discussed but also provide opportunities for hands-on practice. Readers can apply statistical methods to actual data sets, improving their understanding and skills in data analysis and interpretation. -
pergunta:
What software or tools does this book recommend for statistical analysis?
responda: The book recommends popular statistical software tools such as R and Python, which are widely used for statistical analysis in the field of computer science. It provides examples of how to implement the statistical concepts through coding, making it a valuable guide for those looking to develop practical programming skills alongside theoretical knowledge. -
pergunta:
Can this book help with preparing for data science interviews?
responda: Yes, this book can serve as a valuable resource for preparing for data science interviews. It covers essential probability and statistics topics often discussed in technical interviews. By mastering the concepts outlined in this book, candidates can approach interview questions with confidence and demonstrate their analytical skills effectively. -
pergunta:
Are there any online resources or supplementary materials available with this book?
responda: Yes, many versions of this book include supplemental online resources such as lecture notes, additional exercises, and video tutorials. The availability of these materials can enrich the learning experience, providing further clarification on difficult topics and enhancing understanding through varied instructional methods. -
pergunta:
How does this book compare to other statistics books for computer science?
responda: Probability and Statistics for Computer Science stands out due to its targeted approach towards computer scientists. In contrast to more general statistics books, it integrates examples from computer science and focuses on applications relevant to the field. This specialized perspective ensures that readers can directly apply the concepts in their projects and work scenarios. -
pergunta:
Can educators use this book for teaching courses on statistics?
responda: Certainly, this book can be an excellent primary or supplementary text for teaching statistics in computer science. Its structured format, combined with clear explanations and examples, makes it a suitable choice for classroom settings. Educators can utilize its exercises and practical applications to enhance student engagement and comprehension of the material. -
pergunta:
Where can I buy Probability and Statistics for Computer Science 1st ed. 2018 Edition in Brazil?
responda: You can purchase Probability and Statistics for Computer Science 1st ed. 2018 Edition from Ubuy in Brazil. Ubuy offers a convenient shopping experience with a variety of options for obtaining essential academic resources and literature, making it a reliable choice for students and professionals alike.
Probability & Statistics Editorial Review
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Prós
- Clear explanations of concepts
- Useful for beginners
- Good range of examples
- Well-organized chapters
- Engaging exercises
Contras
- Some topics lack depth.
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R$ 532
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Características e benefícios
- Comprehensive background in data analysis, probability, random variables, and statistical methods
- Includes topics like machine learning, simulation, classification, regression, and clustering
- Illustrated throughout with worked examples and pedagogical elements
- Problems and Programming Exercises at the end of each chapter
- Instructor resources available with model solutions and presentation slides
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