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Nº de artículo:
64020786
Domain-Specific Languages in R: Advanced Statistical Programming, (Paperback)
Nº de artículo:
64020786
ARS 95099
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Gain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions.
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What Stands Out
Specialized Focus
Covers domain-specific languages tailored for advanced statistical programming, providing in-depth insights and unique coding techniques to tackle specific analysis challenges in R.
Practical Applications
Includes real-world examples and practical applications, enabling readers to directly apply concepts and improve their statistical analysis capabilities with R.
Expert Guidance
Written by experienced authors, offering expert insights and best practices in statistical programming, ensuring high-quality content and valuable resources for both learners and professionals.
Detalles de producto
- Accelerated introduction to domain-specific languages in R, covering regular expressions
- Shows how DSLs are specialized programming languages for specific purposes
- Includes examples of DSLs for large data sets, matrix multiplication, pattern matching, and continuous time Markov chains
- Teaches how to write DSLs in R and transfer skills to other programming languages
- Intended for individuals with prior programming experience, R knowledge is helpful but not required
- Author: Thomas Mailund, ISBN: 9781484235874, Format: Paperback, Publication Date: 2018-06-24
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | June, 2018 |
| Pages | 257 |
| Subgenre | Languages |
| Series title | No Series |
| Edition | 1 |
| Publisher | Apress |
| Original languages | English |
| Language | English |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.14 x 0.56 x 9.21 in (15.6 x 1.4 x 23.4 cm) |
| Assembled product weight | 0.84 lb (380 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Statistical Analysts
Ideal for analysts who require advanced statistical programming techniques to execute complex data analysis in R effectively.
-
R Programmers
Great for R developers looking to enhance their skills in creating domain-specific languages for customized statistical applications.
-
Data Scientists
Useful for data scientists needing to implement specialized programming languages tailored to specific domains for streamlined analysis.
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Beginners in R
Not suitable for beginners lacking foundational knowledge in R programming and statistical concepts needed for understanding content.
DESCRIPCIÓN DEL PRODUCTO
Domain-Specific Languages in R: Advanced Statistical Programming, (Paperback)
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Thomas Mailund All Books Editorial Review
Domain-Specific Languages in R: Advanced Statistical Programming is a non-fiction paperback that was published in June 2018. With a focus on computing and internet themes, it spans 257 pages and is intended for readers interested in advanced statistical programming concepts using R. The book is bound with perfect binding, making it suitable for thorough reading without risking pages coming loose. Readers often appreciate its clarity and practical applications in real-world scenarios, making it a valuable resource for anyone looking to deepen their knowledge in programming with R.
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ventajas
- Detailed exploration of R programming techniques
- Helpful for both beginners and advanced users
- Well-structured content for easy understanding
- Practical examples to illustrate complex concepts
- Suitable for various computing-related fields
Contras
- Some readers may find it a bit technical
Product Price History
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características y beneficios
- Accelerated introduction to domain-specific languages in R.
- Covers essential topics like regular expressions.
- Learn to write DSLs specialized for specific tasks.
- Explore practical examples in data science and programming.
- Enhance programming skills applicable to other languages.
- Designed for individuals with prior programming experience.
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