The repository holds my work in taking a self taught approach towards my dip into the world of data science. As of early 2019, I work as a data analyst and hope to become a data scientist by 2021.
The main source material on this learning Journey comes from An Introduction to Statistical Learning with Applications in R by the authors
- Gareth James
- Daniela Witten
- Trevor Hastie
- Robert Tibshirani
The contents of this project come directly from the book and thus would include -
- Statistical Learning
- Linear Regression
- Classification
- Resampling Methods
- Linear Model Selection and Regularization
- Moving Beyond Linearity
- Tree-Based Methods
- Support Vector Machines
- Unsupervised Learning
Each topic would contain a Lab file that has notes and lab code and an Applied file that contains answers to exercises from the books.
The following pre-requisites cover installations and packages ->
- tidyverse
- ISLR
- MASS
- boot
- ggplot2
- dplyr
-
An Introduction to Statistical Learning with Applications in R - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani ; 2013
-
Statistical Learning Notes (Series) - Ankit Rathi