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Item #:
67922316
Core Data Analysis: Summarization, Correlation, and Visualization (Undergraduate Topics in Computer Science)
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Item #:
67922316
NZD 163
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What Stands Out
Comprehensive Learning
Offers a thorough understanding of Core Data analysis, preparing students for practical applications in various fields, enhancing their analytical skills for real-world problem-solving.
Visually Engaging
Incorporates advanced visualization techniques that help students easily interpret complex data, making their presentations standout and improving retention of key concepts.
Real-World Applications
Focuses on summarization and correlation in real-world datasets, equipping students with relevant skills that employers highly value in data-driven industries.
Product Details
| Publisher | Springer |
| Publication date | April 18, 2019 |
| Edition | Second Edition 2019 |
| Language | English |
| Print length | 539 pages |
| ISBN-10 | 3030002705 |
| ISBN-13 | 978-3030002701 |
| Item Weight | 1.7 pounds (770 grams) |
| Dimensions | 6.1 x 1.22 x 9.25 inches (15.5 x 3.1 x 23.5 cm) |
| Part of series | Undergraduate Topics in Computer Science |
Who Should Buy?
-
Undergraduate Students
Ideal for undergraduates studying computer science, enhancing their understanding of data analysis and visualization techniques.
-
Data Enthusiasts
Perfect for individuals interested in data analysis who seek foundational knowledge and practical skills in correlation and summarization.
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Educators and Instructors
Useful for educators aiming to incorporate data analysis concepts into their curriculum, facilitating effective teaching strategies.
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Advanced Researchers
Not suitable for advanced researchers requiring sophisticated techniques and methodologies beyond the scope of undergraduate topics.
Product Description
Core Data Analysis: Summarization, Correlation, and Visualization (Undergraduate Topics in Computer Science)
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Intelligence & Semantics Editorial Review
Core Data Analysis: Summarization, Correlation, and Visualization (Undergraduate Topics in Computer Science) offers an insightful approach to understanding complex data through its second edition published by Springer. This comprehensive book covers fundamental techniques for data summarization and correlation, making it a valuable resource for students and professionals alike. With 539 pages of content, it presents a thorough overview of data visualization methods that can aid in interpreting and presenting data effectively. Students have found this edition particularly beneficial for its clarity and depth, making it easier to grasp intricate concepts in data analysis.
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Pros
- Comprehensive coverage of data analysis techniques
- Detailed visualization methods explained
- Clear language suitable for undergraduate students
- Second edition includes updated content
- Well-organized for academic purposes
Cons
- Some readers may find it dense for beginners
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