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Item #:
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
Item #:
87963000
MAD 257
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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.
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?
-
Beginners in Data Science
Perfect for those starting in data science, providing foundational knowledge and practical skills for Kaggle competitions.
-
Python Enthusiasts
Ideal for individuals already familiar with Python who want to apply their coding skills to data analysis.
-
Kaggle Participants
Great resource for users looking to improve their performance on Kaggle by understanding the competition process.
-
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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MAD 257
Order now and get it around Saturday, October 10
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Features & Benefits
- 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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