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Item #:
90224806
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
Item #:
90224806
XCD 156
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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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What Stands Out
Comprehensive Learning
Covers both Python and Spark, offering a deep understanding of data analytics and machine learning techniques, making it ideal for beginners and experienced data professionals.
Hands-On Approach
Includes practical examples and real-world projects, enabling readers to apply concepts immediately and solidify their learning through practice.
Focused on Scale
Specifically designed for large-scale data analysis, empowering users to tackle big data challenges efficiently, setting it apart from traditional analytics guides.
Product Details
| 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 |
| Country of Origin | This item will be imported from US |
| Date First Available | October 29, 2023 |
| What is in the box | Large-Scale Data Analytics with... For more details, please check description/product details |
Who Should Buy?
-
Data Scientists
Ideal for data scientists aiming to enhance their skills in machine learning using Python and Spark frameworks.
-
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.
Product Description
Large-Scale Data Analytics with Python and Spark: A Hands-On Guide to Implementing Machine Learning Solutions, (Paperback)
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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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Pros
- Hands-on guide for practical learning
- Covers machine learning implementations
- Well-structured for educational purposes
- Informative for database administration
- Suitable for college-level readers
Cons
- No collectible value noted
Product Price History
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XCD 156
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Features & Benefits
- 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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