RAVINDRA NAYAK 的 KDP 数据
RAVINDRA NAYAK 在 Amazon 有 126 本书，近 30 天有 39 本上过榜，最好排名 #2，主要在Computer Mathematics、应用、数学。
126Amazon 上的书
39近 30 天上榜书
4.7平均评分
| 价格中位数 | $3.99 |
|---|---|
| 价格区间（中间 50%） | $3.99 – $5.99 |
| 评论中位数 | 1 |
| 评论总数 | 7 |
| 近 30 天新书 | 30 |
| 近 30 天突围新书 | 11 |
数据解读
- 定价中位 $3.99，比主类目「Computer Mathematics」的中位价低 50%。
- 近 30 天新上 30 本，其中 11 本冲进了畅销榜。
RAVINDRA NAYAK 排名最好的书
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Context Engineering (THE SUPER AGENT JOURNEY From Absolute Zero to AI Orchestrator Book 13)
作者 RAVINDRA NAYAK
- 价格$3.99
- 评论-
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Python First Principles for Data Scientists and Developers Volume 8: The AI and Automation Workshop (Think in Python: A First-Principles Ladder to Data ... Zero Fear to Research-Ready Code Book 9)
作者 RAVINDRA NAYAK
- 价格$5.99
- 评论-
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"Branches of Choice: A Decision Tree Novel for the Intelligent Mind"
作者 RAVINDRA NAYAK
- 价格$3.99
- 评论-
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Python First Principles for Data Scientists and Developers: Volume 4: The Data Scientist's Python Workbench (Think in Python: A First-Principles Ladder ... Zero Fear to Research-Ready Code Book 5)
作者 RAVINDRA NAYAK
- 价格$5.99
- 评论-
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MCP: A Common Language for AI and Tools (THE SUPER AGENT JOURNEY From Absolute Zero to AI Orchestrator Book 24)
作者 RAVINDRA NAYAK
- 价格$3.99
- 评论-
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The Probability Detective: Naive Bayes from First Principles (Patterns Before Predictions A Fear-Free Machine Learning Journey from First Principles Book 5)
作者 RAVINDRA NAYAK
- 价格$3.99
- 评论-
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RAG 2.0: From Document Search to Intelligent Knowledge Systems (THE SUPER AGENT JOURNEY From Absolute Zero to AI Orchestrator Book 20)
作者 RAVINDRA NAYAK
- 价格$3.99
- 评论-
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Before Artificial Intelligence: What Does It Mean to Solve Something? (THE SUPER AGENT JOURNEY From Absolute Zero to AI Orchestrator Book 1)
作者 RAVINDRA NAYAK
- 价格$3.99
- 评论-
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Vectors Without Fear (THE SUPER AGENT JOURNEY From Absolute Zero to AI Orchestrator Book 15)
作者 RAVINDRA NAYAK
- 价格$3.99
- 评论-
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"Mastering Forecasting in Machine Learning: A Journey through Crude Oil and Beyond": A Journey through Crude Oil and Beyond
作者 RAVINDRA NAYAK
- 价格$7.99
- 评论-
主要上榜类目
数据说明
数据来自 KDPTop 每天对 Amazon 公开榜单页面的抓取，中位数基于最新一期畅销榜。机会分综合了评论门槛、低评论书占比、榜单更替、新书占比和价格，0–100 分，越高越容易进入。