A termék adatai:
ISBN13: | 9781484281543 |
ISBN10: | 1484281543 |
Kötéstípus: | Puhakötés |
Terjedelem: | 511 oldal |
Méret: | 254x178 mm |
Súly: | 1016 g |
Nyelv: | angol |
Illusztrációk: | 100 Illustrations, black & white |
515 |
Témakör:
Programszerkesztő és -fordító eszközök (Compilers and Interpreters)
Magasszintű programnyelvek
Programnyelvek általában
Programszerkesztő és -fordító eszközök (Compilers and Interpreters) (karitatív célú kampány)
Magasszintű programnyelvek (karitatív célú kampány)
Programnyelvek általában (karitatív célú kampány)
Beginning Data Science in R 4
Data Analysis, Visualization, and Modelling for the Data Scientist
Kiadás sorszáma: 2nd ed.
Kiadó: Apress
Megjelenés dátuma: 2022. június 24.
Kötetek száma: 1 pieces, Book
Normál ár:
Kiadói listaár:
EUR 58.84
EUR 58.84
Az Ön ára:
19 424 (18 499 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 4 856 Ft)
A kedvezmény érvényes eddig: 2024. június 30.
A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
Kattintson ide a feliratkozáshoz
Kattintson ide a feliratkozáshoz
Beszerezhetőség:
Becsült beszerzési idő: A Prosperónál jelenleg nincsen raktáron, de a kiadónál igen. Beszerzés kb. 3-5 hét..
A Prosperónál jelenleg nincsen raktáron.
Nem tudnak pontosabbat?
A Prosperónál jelenleg nincsen raktáron.
Rövid leírás:
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.
Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You?ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.
This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming.
What You Will Learn- Perform data science and analytics using statistics and the R programming language
- Visualize and explore data, including working with large data sets found in big data
- Build an R package
- Test and check your code
- Practice version control
- Profile and optimize your code
- Perform data science and analytics using statistics and the R programming language
- Visualize and explore data, including working with large data sets found in big data
- Build an R package
- Test and check your code
- Practice version control
- Profile and optimize your code
Hosszú leírás:
Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well.
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.
Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You?ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.
Source code is available at github.com/Apress/beg-data-science-r4.
What You Will Learn
- Perform data science and analytics using statistics and the R programming language
- Visualize and explore data, including working with large data sets found in big data
- Build an R package
- Test and check your code
- Practice version control
- Profile and optimize your code
Who This Book Is For
Those with some data science or analytics background, but not necessarily experience with the R programming language.
Tartalomjegyzék:
1: Introduction.- 2: Introduction to R Programming.- 3: Reproducible Analysis.- 4: Data Manipulation.- 5: Visualizing Data.- 6: Working with Large Data Sets.- 7: Supervised Learning.- 8: Unsupervised Learning.- 9: Project 1: Hitting the Bottle.- 10: Deeper into R Programming.- 11: Working with Vectors and Lists.- 12: Functional Programming.- 13: Object-Oriented Programming.- 14: Building an R Package.- 15: Testing and Package Checking.- 16: Version Control.- 17: Profiling and Optimizing.- 18: Project 2: Bayesian Linear Progression.- 19: Conclusions.