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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
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Practical Statistics for Data Scientists is a must-read for anyone who wants to implement statistical methods in practical data science projects. Comprehensive examples in R and Python are included, and the book's focus on the practical aspects of statistics is refreshing.
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Product Details
- Comprehensive examples in Python and practical guidance on applying statistical methods to data science
- Covers important concepts like exploratory data analysis, random sampling, experimental design, regression, classification techniques, and statistical machine learning methods
- Bridges the gap for individuals with familiarity in R or Python programming and exposure to statistics
- Authors are experienced in both data science and statistics, providing valuable insights and code examples in R and Python
- Accessible, readable format suitable for data scientists aiming to deepen their statistical perspective
- Book's goals include laying out key statistics concepts relevant to data science and explaining their importance and usefulness
| Publisher | O'Reilly Media |
| Publication date | June 16, 2020 |
| Edition | 2nd |
| Language | English |
| Print length | 360 pages |
| ISBN-10 | 149207294X |
| ISBN-13 | 978-1492072942 |
| Item Weight | 1.35 pounds (610 grams) |
| Dimensions | 7 x 0.9 x 9.1 inches (17.8 x 2.3 x 23.1 cm) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for those entering the field, offering foundational statistical concepts applied in R and Python.
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Practicing Analysts
Beneficial for current data analysts seeking to enhance their statistical skills and improve their data insights.
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Students in Statistics
Perfect for undergraduate or graduate students who need practical applications of statistics in data science.
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Beginners in Programming
Not suitable for individuals with no programming background, as concepts require familiarity with R and Python.
Product Description
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
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Mathematical Analysis Editorial Review
The book, although having good content, disappointed some customers due to its low quality print. The illustrations printed in black and white looked faded and the paper quality was cheap. However, the technical content was comprehensive and informative. One customer was pleased with the fast and efficient service they received after receiving a book with missing pages. The book covers R and Python content with concise and quick-to-the-point code. The book is great for beginners and those new to statistics but provides a great refresher to those looking to review some concepts. The book emphasizes practical application of statistical concepts and is great for those who want to understand the meaning behind statistical tools and applications before getting into the mathematical weeds.
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Pros
- Comprehensive and informative technical content
- Concise and quick-to-the-point code for R and Python content
- Great for beginners and those new to statistics
- Practical application of statistical concepts
Cons
- Low quality print with illustrations printed in black and white
Product Price History
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Features & Benefits
- Comprehensive examples in Python
- Practical guidance on applying statistical methods
- Accessible, readable format
- Key preliminary step in data science
- Classification techniques for predicting
- Unsupervised learning methods
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