Title
Practical Statistics for Data Scientists: 50 Essential Concepts,New
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Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you?re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you?ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that 'learn' from data Unsupervised learning methods for extracting meaning from unlabeled data
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- Q: How many pages does this book have? A: This book has three hundred fifteen pages. It provides a thorough overview of essential statistical concepts for data scientists.
- Q: What is the binding type of the book? A: The book is paperback bound. This makes it lightweight and easy to handle for both reading and reference.
- Q: What are the dimensions of the book? A: The book measures six point seventy-five inches in length, zero point fifty-one inches in width, and nine point zero two inches in height. This size is convenient for both reading and portability.
- Q: How do I apply the statistical methods in this book? A: You can apply the statistical methods by following the practical examples provided. The book explains how to implement these methods within data science projects.
- Q: Is this book suitable for beginners in data science? A: Yes, this book is suitable for beginners. It explains concepts in an accessible manner, making it easy for those with basic knowledge of statistics to understand.
- Q: What background knowledge do I need to read this book? A: A basic understanding of statistics and familiarity with the R programming language is recommended. This will help you grasp the concepts more effectively.
- Q: How should I store this book to keep it in good condition? A: Store the book in a dry, cool place away from direct sunlight. This will help preserve its condition and prevent any damage over time.
- Q: Can I clean the book if it gets dirty? A: Yes, you can clean the book gently with a soft, dry cloth. Avoid using water or cleaners, as they may damage the pages.
- Q: Is the book safe for children? A: Yes, the book is safe for children. However, it is recommended for older children and teenagers who have an interest in data science and statistics.
- Q: What makes this book different from other statistics books? A: This book focuses specifically on applying statistical methods to data science, which is often lacking in traditional statistics texts. It bridges the gap between theory and practical application.
- Q: How can I use this book for self-study? A: You can use this book for self-study by following the structured chapters and practical examples. It offers clear explanations and exercises to reinforce learning.
- Q: Is there a warranty or return policy for this book? A: Yes, there is a return policy. If the book arrives damaged or not as described, you may return it for a refund or exchange.
- Q: What if I have issues with my order? A: If you have issues with your order, contact customer support for assistance. They can help resolve any problems related to shipping or product condition.
- Q: Can I find similar topics in this book? A: Yes, the book covers a variety of topics related to data science and statistics, including exploratory data analysis, regression, and classification techniques.
- Q: Is there an index or glossary in the book? A: Yes, the book includes an index and glossary to help readers quickly find specific topics and terms related to statistical methods.