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31161279
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, (Paperback)
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Item #:
31161279
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What Stands Out
Cross-Platform Tools
Utilizes both R and Python, offering flexibility and accessibility for data scientists, enabling them to choose the best tool for their specific tasks and projects.
Essential Concepts
Covers over 50 critical statistical concepts, ensuring a solid foundation for understanding and applying statistics in real-world data science scenarios, enhancing practical skills.
Practical Examples
Includes practical examples and applications that help translate theoretical concepts into actionable insights, making it easier for readers to apply statistical techniques effectively.
Product Details
- Comprehensive examples in Python and practical guidance for applying statistical methods to data science
- Emphasis on avoiding the misuse of statistical methods and focusing on what's important
- Bridges the gap between data science and statistics in an accessible format for R and Python users
- Covers key preliminary steps in data science like exploratory data analysis and random sampling
- Includes techniques for regression, experimental design, classification, and statistical machine learning
- Provides insight into unsupervised learning methods for extracting meaning from unlabeled data
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | June, 2020 |
| Pages | 360 |
| Reading level | Professional and Scholarly |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 2nd Edition |
| Publisher | O'Reilly Media |
| Original languages | English |
| Language | English |
| Edu focus | Mathematics |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.00 x 0.90 x 9.10 in (17.8 x 2.3 x 23.1 cm) |
| Assembled product weight | 1.3 lb (590 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Data Science Students
This book is ideal for students learning data science concepts and wanting practical applications using R and Python.
-
Professional Analysts
Data analysts looking to enhance their statistical knowledge and apply it practically will benefit from this book.
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Self-Learners
Individuals teaching themselves data science can leverage the clear explanations and examples to grasp essential statistical concepts.
-
Beginner Statisticians
Those new to statistics may find the content too advanced without a foundational understanding of the subject.
Product Description
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, (Paperback)
About This Item
Introducing "Practical Statistics for Data Scientists: 50 Essential Concepts Using R and Python Edition 2 Paperback" – the comprehensive guidebook that every aspiring and experienced data scientist needs in their arsenal. Authored by Peter Bruce, Andrew Bruce, and Peter Gedeck, this book is an invaluable resource for anyone looking to master the world of data science. With the rapid advancement of technology and the ever-increasing importance of data-driven decision making, proficiency in statistics is no longer optional for data scientists. This book covers 50 essential statistical concepts, providing you with a solid foundation to solve real-world problems using R and Python. Whether you are a beginner just starting on your data science journey or a seasoned professional seeking to expand your knowledge, this book has something for everyone.
The authors have carefully curated the content to ensure that the concepts are presented in a practical and accessible manner, making it easy for readers to grasp and apply statistical techniques. The publication date of May 2020 ensures that this book is up-to-date with the latest advancements and best practices in the field of data science. It is formatted as a paperback, making it convenient for you to carry it with you wherever you go. By delving into the pages of "Practical Statistics for Data Scientists," you will gain a deep understanding of statistical concepts such as hypothesis testing, regression analysis, machine learning, and much more. The authors provide clear explanations, step-by-step examples, and code snippets in R and Python, allowing you to implement what you learn directly in your projects. With its focus on practical application, this book is a must-have for any data scientist.
Gain the knowledge and skills you need to excel in your career, enhance your decision-making abilities, and unlock valuable insights from your data. Don't miss out on this opportunity to enhance your data science toolkit. Order your copy of "Practical Statistics for Data Scientists: 50 Essential Concepts Using R and Python Edition 2 Paperback" today and embark on your journey towards becoming a master data scientist.
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Customer Questions & Answers
-
Question:
Does the book cover basic statistics?
Answer: Yes, the guide covers basic statistics from a data science perspective. -
Question:
What is the format of the book?
Answer: The book is available in paperback format. -
Question:
Who are the authors of the book?
Answer: The authors of the book are Peter Bruce, Andrew Bruce, and Peter Gedeck.
Peter Bruce All Books Editorial Review
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python is a comprehensive guide designed for professionals looking to deepen their understanding of data science concepts through practical examples. This 2nd edition paperback, published by O'Reilly Media in June 2020, spans 360 pages, making it a robust resource for both R and Python users. Readers have praised its clear explanations and real-world applications, making complex statistical concepts more accessible and applicable in data-driven environments. Ideal for those in computing and internet fields, this book serves as a valuable addition to any data scientist's library.
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Pros
- Includes practical examples in R and Python
- Covers essential concepts for data science
- Clear explanations for complex topics
- Designed for professional reading levels
- Published by a reputable source
Cons
- Might be too advanced for beginners
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Features & Benefits
- Comprehensive examples in Python
- Avoiding misuse of statistical methods
- Advice on what's important and what's not
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