SØG - mellem flere end 8 millioner bøger:
Viser: Data Science in R
Beginning Data Science in R Vital Source e-bog
Thomas Mailund
(2017)
Beginning Data Science in R Vital Source e-bog
Thomas Mailund
(2017)
Data Science in R
Thomas Mailund
(2017)
Sprog: Engelsk
Detaljer om varen
- Vital Source searchable e-book (Fixed pages)
- Udgiver: Springer Nature (Marts 2017)
- ISBN: 9781484226711
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Vital Source 365 day rentals (fixed pages)
- Udgiver: Springer Nature (Marts 2017)
- ISBN: 9781484226711R365
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: 5 år fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Paperback
- Udgiver: Apress L. P. (Marts 2017)
- ISBN: 9781484226704
Beginning Data Science in R 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
Who This Book Is For
Those with some data science or analytics background, but not necessarily experience with the R programming language.
1. Introduction to R programming.
2. Reproducible analysis.
3. Data manipulation.
4. Visualizing and exploring data.
5. Working with large data sets.
6. Supervised learning.
7. Unsupervised learning.
8. More R programming.
9. Advanced R programming.
10. Object oriented programming.
11. Building an R package.
12. Testing and checking.
13. Version control.
14. Profiling and optimizing.