Practical Data Science Series Kaggle Start Book in Python Tankobon Hardcover – Big Book, March 19, 2020
81% of respondents would recommend this to a friend
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Product Details
| Publisher | u8b1bu8ac7u793e (March 19, 2020) |
| Publication date | March 19, 2020 |
| Language | Japanese |
| Tankobon Hardcover | 192 pages |
| ISBN-10 | 4065190061 |
| ISBN-13 | 978-4065190067 |
| Dimensions | 7.2 x 0.39 x 9.17 inches (18.3 x 1 x 23.3 cm) |
Who Should Buy?
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Beginners in Data Science
Perfect for those starting in data science, providing foundational knowledge and practical skills for Kaggle competitions.
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Python Enthusiasts
Ideal for individuals already familiar with Python who want to apply their coding skills to data analysis.
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Kaggle Participants
Great resource for users looking to improve their performance on Kaggle by understanding the competition process.
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Advanced Data Scientists
Not suitable for experienced professionals seeking advanced concepts and methodologies beyond Kaggle basics.
Product Description
Practical Data Science Series Kaggle Start Book in Python Tankobon Hardcover – Big Book, March 19, 2020
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石原 祥太郎 , 村田 秀樹 Editorial Review
The "PythonではじめるKaggleスタートブック" serves as an accessible and engaging introduction to Kaggle for those who are beginning their journey in data science and machine learning. Reviewers highlight that the book effectively answers common beginner queries about how to earn medals, submit entries, and progress through competitions on Kaggle, making it particularly valuable for those without prior experience in Python or machine learning. The text is noted for its colorful layout and detailed code explanations, which help readers understand the function of the code as they work through the material. Many appreciate the conversational format that includes insights on starting Kaggle, the enjoyment of submissions, building benchmarks, and the importance of feature engineering. The inclusion of diverse data types—such as table, image, and text data—adds to its completeness. While aimed at beginners, the book seems best suited for those with some foundational knowledge of Python and machine learning principles, as several users found the content challenging without such background. Key takes indicate that complete novices may struggle, particularly due to some unclear instructions and potential discrepancies with code execution stemming from version changes over time. Overall, the "PythonではじめるKaggleスタートブック" has been beneficial for many eager learners wanting to engage with Kaggle but might prove overwhelming without the necessary preliminary knowledge. It strikes a balance between being informative and practical, though future editions might benefit from enhanced editing and clearer instructions. **
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Pros
- Clear explanations of key concepts and processes in Kaggle.
- Engaging format with informative commentary on personal experiences.
- Comprehensive coverage of different data types including text and image data.
- Good for beginners who have some foundational knowledge of Python and machine learning.
Cons
- May be too challenging for complete novices without any programming or machine learning background.
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