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Let's get started with my own take on the best Statistical Learning book ever written. All the credit for this book goes to the four authors, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
In 2017, I started heavily utilizing the materials in this textbook with my undergraduate degree and freelance data science career. I learned quickly that this textbook was very good at showing readers how to utilize statistical methods without being to theoretical but leaving readers with a true understanding of these methods. In late 2021, version 2.0 was released.
Fast forward to 2022, I have decided to begin studying for the Society of Actuaries ASA Data Science Micro Credential, which requires an exam known as "Statistics for Risk Modeling". This was the perfect chance for me to revisit this textbook, as the material for the exam heavily revolves around the material in this textbook. I like to use my own examples as I parse through a textbook. I'll write about them here!