Foundations of statistics for data scientists: with R and Python
Agresti, Alan
Foundations of statistics for data scientists: with R and Python - Boca Raton CRC Press 2022 - xvii, 467p.
https://www.routledge.com/Foundations-of-Statistics-for-Data-Scientists-With-R-and-Python/Agresti-Kateri/p/book/9780367748456?srsltid=AfmBOoqd7kNzZMKD4vRkjMaiF
"Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on ""why it works"" as well as ""how to do it."" Compared to traditional ""mathematical statistics"" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.
Key Features:
Shows the elements of statistical science that are important for students who plan to become data scientists.
Includes Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python).
Contains nearly 500 exercises."
9780367748456 (hb.)
Programming Languages
Statistical Methods
Quantitative Research
519.50285536 / AGR
Foundations of statistics for data scientists: with R and Python - Boca Raton CRC Press 2022 - xvii, 467p.
https://www.routledge.com/Foundations-of-Statistics-for-Data-Scientists-With-R-and-Python/Agresti-Kateri/p/book/9780367748456?srsltid=AfmBOoqd7kNzZMKD4vRkjMaiF
"Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on ""why it works"" as well as ""how to do it."" Compared to traditional ""mathematical statistics"" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.
Key Features:
Shows the elements of statistical science that are important for students who plan to become data scientists.
Includes Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python).
Contains nearly 500 exercises."
9780367748456 (hb.)
Programming Languages
Statistical Methods
Quantitative Research
519.50285536 / AGR