Large-Scale Data Analytics with Python and Spark: A Hands-On Guide to Implementing Machine Learning Solutions, (Paperback)
94% of respondents would recommend this to a friend
HKD 454
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This hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data.
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產品詳情
- Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | February, 2024 |
| Pages | 422 |
| Reading level | General |
| Subgenre | Database Administration & Management |
| Series title | No Series |
| Edition | 1 |
| Publisher | Cambridge University Press |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Educational level | College |
| Is collectible | N |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.69 x 0.94 x 9.53 in (17 x 2.4 x 24.2 cm) |
| Assembled product weight | 1.7 lb (770 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
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Data Scientists
Ideal for data scientists aiming to enhance their skills in machine learning using Python and Spark frameworks.
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Machine Learning Enthusiasts
Perfect for learners eager to understand machine learning applications through practical examples and hands-on projects.
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Big Data Engineers
Beneficial for engineers seeking to implement scalable data analytics solutions effectively using modern technologies.
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Total Beginners
Not suitable for individuals with no prior coding or data analysis knowledge as it requires some foundational skills.
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Isaac Triguero All Books Editorial Review
Large-Scale Data Analytics with Python and Spark: A Hands-On Guide to Implementing Machine Learning Solutions is a comprehensive resource for those keen on mastering data analytics techniques. This non-fiction paperback, published by Cambridge University Press, spans 422 pages and is aimed at a general reading level, making it accessible for college students and professionals alike. It delves into essential topics within the scope of database administration and management, presenting delightful insights into implementing machine learning solutions using modern tools like Python and Spark, which readers often find engaging and informative. It is designed to equip readers with practical skills in technology, perfect for those in the computing and internet domain.
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優點
- Hands-on guide for practical learning
- Covers machine learning implementations
- Well-structured for educational purposes
- Informative for database administration
- Suitable for college-level readers
缺點
- No collectible value noted
Product Price History
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HKD 454
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特色和優勢
- Comprehensive guide on large-scale data analytics with Python and Spark.
- Focuses on essential principles without overwhelming details.
- Includes real-world examples and hands-on coding exercises.
- Well-defined learning objectives and exercises with online solutions.
- Accompanies lectures with structured lab exercises in Jupyter Notebooks.
- Ideal for graduate-level courses in computer/data science departments.
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