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87963000
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
Nº de artículo:
87963000
COP 98459
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What Stands Out
Beginner-Friendly Guide
Designed specifically for beginners, this book simplifies complex data science concepts, making it accessible for those new to Python and Kaggle.
Hands-On Projects
Includes practical projects that allow readers to apply their knowledge, enabling effective learning through real-world data science challenges.
Up-to-Date Resources
Offers the latest practices and tools used in the field, ensuring readers are equipped with contemporary skills necessary for success in data science.
Detalles de producto
| 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.
DESCRIPCIÓN DEL PRODUCTO
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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ventajas
- 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.
Contras
- May be too challenging for complete novices without any programming or machine learning background.
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COP 98459
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características y beneficios
- The definitive introductory book for beginners in Kaggle.
- Teaches practical skills to participate in actual competitions.
- Includes detailed explanations of sample code for better understanding.
- Provides insights from winning teams and experienced Kaggler.
- Ideal for those seeking systematic knowledge in data science.
- Accessible support through honest conversations.
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