En el vertiginoso mundo de la ciencia de datos, la eficiencia y la rapidez son clave. Aquí es donde el libro Ciencias de datos para estudiantes se convierte en tu aliado perfecto. Este recurso está diseñado para proporcionarte acceso rápido a las funciones y librerías más cruciales de Python y aplicadas a la realidad de los negocios.

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis. 

The first edition of this book, with applications in R (ISLR), was released in 2013. A 2nd Edition of ISLR was published in 2021. It has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian, and Vietnamese. The Python edition (ISLP) was published in 2023.

The chapters cover the following topics:
What is statistical learning?

• Regression

• Classification

• Resampling methods

• Linear model selection and regularization

• Moving beyond linearity

• Tree-based methods

• Support vector machines

• Deep learning

• Survival analysis

• Unsupervised learning

• Multiple testing