Large-Scale Data Analytics with Python and Spark: A Hands-On Guide to Implementing Machine Learning Solutions, Paperback
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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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Podrobnosti produkta
| 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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Large-Scale Data Analytics with Python and Spark: A Hands-On Guide to Implementing Machine Learning Solutions, Paperback
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€ 53
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Značilnosti in prednosti
- 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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