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Data Mining for Business Analytics: Concepts, Techniques and Applications in Python
85% of respondents would recommend this to a friend
MYR 1535
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This book has by far the most comprehensive review of business analytics methods covering everything from classical approaches to modern methods.
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What Stands Out
Product Details
- Covers data mining concepts and methods using Python for illustration
- Includes statistical and machine learning algorithms for various business applications
- Introduces a new co-author with expertise in teaching business analytics courses in Python
- Contains new section on ethical issues in data mining and more than a dozen case studies
- Provides end-of-chapter exercises and a companion website with data sets and instructor materials
- Ideal for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics
| Publisher | Wiley |
| Publication date | November 5, 2019 |
| Edition | 1st |
| Language | English |
| Print length | 608 pages |
| ISBN-10 | 1119549841 |
| ISBN-13 | 978-1119549840 |
| Item Weight | 2.5 pounds (1.13 kg) |
| Dimensions | 7.2 x 1.2 x 10.1 inches (18.3 x 3 x 25.7 cm) |
Who Should Buy?
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Data Science Students
Ideal for students learning data mining concepts and applications in Python for business-oriented projects.
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Business Analysts
Helps analysts understand data-driven decision-making processes with practical Python applications in business contexts.
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Data Professionals
A valuable resource for professionals seeking to enhance their data mining skills specifically for business analytics.
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Absolute Beginners
Not suitable for those with no prior programming or data analysis experience; requires foundational knowledge.
Product Description
Data Mining for Business Analytics: Concepts, Techniques and Applications in Python
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Business Intelligence Tools Editorial Review
Data Mining for Business Analytics: Concepts, Techniques and Applications in Python is a comprehensive guidebook for individuals longing to learn about data mining techniques to solve business problems. This book is highly informative, providing easy-to-follow concepts alongside examples and the relevant code to accompany them, making it easy for readers to grasp concepts and apply them. However, there are different opinions regarding the readability of the book. There have been complaints regarding the poorly formatted equations and missing characters that may pose difficulty in some parts of the text. The Kindle version is also criticized for being completely unreadable. However, buyers of the physical book are generally satisfied with the quality and learnability of the book.
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Pros
- Provides easy-to-follow concepts and accompanying code
- Quality visual elements
- The physical book is didactic and not wordy
- Comprehensive guidebook for individuals longing to learn about data mining techniques to solve business problems
Cons
- Some equations and characters are poorly formatted or missing
Product Price History
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MYR 1535
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Features & Benefits
- Applied approach to data mining concepts and methods
- Learn how to implement popular data mining algorithms in Python
- Covers both statistical and machine learning algorithms
- New material based on feedback from instructors and students
- Includes more than a dozen case studies
- Ideal for graduate and upper-undergraduate level courses
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