Beginning data science in R 4 : data analysis, visualization, and modelling for the data scientist /
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 softwar...
Clasificación: | Libro Electrónico |
---|---|
Autor principal: | |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
New York, New York :
Apress,
[2022]
|
Edición: | 2nd ed.. |
Temas: | |
Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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.