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Item #:
227572170
Data Science: Concepts and Practice
Item #:
227572170
€ 57
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Product Details
| Publisher | Morgan Kaufmann |
| Publication date | November 27, 2018 |
| Edition | 2nd |
| Language | English |
| File size | 55.5 MB |
| Screen Reader | Supported |
| Enhanced typesetting | Enabled |
| X-Ray for textbooks | Enabled |
| Word Wise | Not Enabled |
| Print length | 544 pages |
| ISBN-13 | 978-0128147627 |
| Page Flip | Enabled |
| Item Weight | 1.5 lbs (680 grams) |
Product Description
Data Science: Concepts and Practice
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AI & Semantics Editorial Review
The product in review appears to be a textbook on Predictive Analytics that utilizes RapidMiner as its primary tool. Customers have generally found the book to be well-written, straightforward, and effective for those specifically looking to learn how to employ RapidMiner in their predictive analytics endeavors. While the book covers foundational concepts in data science, some users note that it may not be comprehensive enough for those seeking an in-depth understanding of the field, particularly in programming languages like R or Python. Feedback highlights the effectiveness of the book in teaching fundamental principles, with one reviewer praising its clarity and ease of understanding. This positions the textbook as an excellent resource for beginners or students needing to engage with predictive analytics in a practical context, albeit with the understanding that it may serve best as a supplement rather than a standalone data science curriculum. **
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Pros
- Comprehensive coverage of using RapidMiner for predictive analytics.
- Well-written and easy to understand, making it accessible for beginners.
- Provides a solid foundation in the principles of data science.
Cons
- Not a deep dive into data science; may not suffice for advanced learning.
Product Price History
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€ 57
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Features & Benefits
- Master 30 powerful data science algorithms.
- Learn data science techniques applicable to businesses.
- Use RapidMiner for hands-on data analysis.
- Understand concepts of data science through simple explanations.
- No prior programming experience required.
- Practical use cases demonstrate effective implementation.
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