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Item #:
3502111
Think Bayes: Bayesian Statistics in Python
Item #:
3502111
QAR 173
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With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation.
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What Stands Out
Comprehensive Coverage
Offers an extensive introduction to Bayesian statistics using Python, making it suitable for both beginners and experienced practitioners seeking to deepen their understanding.
Practical Examples
Includes numerous hands-on examples and exercises, allowing readers to apply Bayesian concepts in real-world scenarios and enhance their programming skills in Python.
Accessible Learning
Written in a clear and engaging style, the book simplifies complex topics, making Bayesian statistics approachable for those without a strong mathematical background.
Product Details
| Item Weight | 1 lbs (450 grams) |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to deepen their understanding of Bayesian statistics and implement models in Python.
-
Statistics Students
Great resource for students studying statistics, offering practical examples and hands-on coding exercises in Python.
-
Machine Learning Enthusiasts
Beneficial for ML practitioners interested in incorporating Bayesian methods into their algorithms for better predictive modeling.
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Beginners in Stats
Not suitable for beginners without a foundational understanding of statistics, as it dives deeply into complex concepts.
Product Description
Think Bayes: Bayesian Statistics in Python
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Customer Questions & Answers
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Question:
Do I need advanced mathematics to understand this book?
Answer: No, a basic understanding of probability is sufficient. -
Question:
What programming skills do I need before starting this book?
Answer: Familiarity with Python programming is required. -
Question:
Can I apply what I learn to real-world problems?
Answer: Yes, the book emphasizes practical applications of Bayesian statistics.
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QAR 173
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Features & Benefits
- Designed for Python programmers with a basic understanding of probability.
- Focuses on solving statistical problems through code rather than complex math.
- Covers Bayesian fundamentals clearly through practical examples.
- Teaches computational methods for real-world applications.
- Includes engaging examples like Dungeons & Dragons and sports.
- Ideal for beginners looking to dive into Bayesian statistics.
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