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Big Data Analytics: Disruptive Technologies for Changing the Game
76% of respondents would recommend this to a friend
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Bringing a practitioner’s view to big data analytics, this work examines the drivers behind big data, identifies use cases, and recommends implementation approaches.
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產品詳情
| Publisher | MC Press |
| Publication date | November 15, 2012 |
| Language | English |
| Print length | 96 pages |
| ISBN-10 | 1583473807 |
| ISBN-13 | 978-1583473801 |
| Item Weight | 4.8 ounces (136.08 grams) |
| Dimensions | 6 x 0.2 x 9 inches (15.2 x 0.5 x 22.9 cm) |
Who Should Buy?
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Data Analysts
Data analysts seeking advanced techniques will benefit from the insights and methodologies outlined in this comprehensive guide.
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Business Leaders
Business leaders looking to leverage big data for strategic decisions can use this book to understand competitive advantages.
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Tech Students
Students studying data science or analytics will find the book an essential resource for understanding disruptive technologies.
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Casual Readers
Individuals looking for light reading may find the technical content overwhelming or excessively detailed for casual interest.
產品描述
Big Data Analytics: Disruptive Technologies for Changing the Game
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Management Information Systems Editorial Review
** Big Data Analytics: Disruptive Technologies for Changing the Game** "Big Data Analytics: Disruptive Technologies for Changing the Game" offers a concise yet informative introduction to the realm of Big Data, tailored primarily for business executives and managers. At just 73 pages, the book is succinct, ensuring that every sentence contributes meaningful insights rather than overwhelming the reader with unnecessary jargon or details. Reviewers noted that the author skillfully avoids excessive complexity, making it a valuable resource for those looking to grasp the foundational concepts of Big Data without diving into technical algorithms. One of the standout features of this book is its structured approach, where key chapters effectively outline applications, architectural components, and implementation strategies pertaining to Big Data analytics. Business users particularly appreciate how the book contextualizes Big Data within existing B2B and B2C landscapes, elucidating why it poses both opportunities and challenges. This aligns well for executives who need to discuss and navigate Big Data strategies without acting as direct practitioners. Moreover, while some readers suggested that data scientists may find limited value in this resource, it nonetheless provides essential vernacular and foundational knowledge that can foster productive discussions among teams. The practical business use cases and the identification of problems within the chapters enhance its role as a reference tool. Overall, the book has been regarded as an excellent starting point for those beginning their journey in understanding Big Data. However, the brevity of the book has led some reviewers to feel that those seriously Considering Big Data in their organizations may benefit from more extensive texts. It is a straightforward read, presenting clear insights that are critical for both business and IT leaders, but may not satisfy those looking for in-depth technical guidance. **
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優點
- Clear and concise presentation of Big Data concepts.
- Engaging writing style that appeals to business audiences.
- Useful for business managers and executives needing foundational knowledge of Big Data.
- Effective case studies and practical applications of Big Data analytics.
- Structured layout with key themes clearly outlined.
缺點
- Very brief at only 73 pages, may leave readers wanting more depth.
Product Price History
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TWD 579
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特色和優勢
- Gain insights from a practitioner’s perspective on big data analytics.
- Explore key drivers and use cases that define big data applications.
- Learn about recommended implementation strategies for effective analytics.
- Understand how big data is transforming analytics architecture.
- Benefit from the author's 20 years of experience in the field.
- Stay ahead with the latest methodologies in big data analysis.
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