Foundations of Statistics for Data Scientists
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ISBN-100367748452
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ISBN-13978-0367748456Principles of Statistics for Data Analysts: With R and Python was crafted as a textbook for a one- or two-term introduction to mathematical statistics for university students aspiring to become data professionals. It offers a comprehensive exploration of the topics in statistical science that every data expert should be familiar with, including probability distributions, descriptive and inferential statistical techniques, and linear modeling. The book assumes knowledge of basic calculus, allowing the presentation to address “why it works” as well as “how to execute it.” In comparison to traditional “mathematical statistics” textbooks, the book places less emphasis on probability theory and more focus on utilizing software to implement statistical techniques and conduct simulations to illustrate key concepts. All statistical analyses in the book are performed using R software, with an appendix demonstrating the same analyses using Python.
Key Features:
- Illustrates the essentials of statistical science crucial for university students pursuing careers as data analysts.
- Incorporates Bayesian and regularized fitting of models (e.g., showcasing an example using the lasso), classification and clustering, and implementation of techniques using modern software (R and Python).
- Includes nearly 500 exercises.
The book also introduces contemporary topics that are not commonly found in mathematical statistics texts but are highly relevant for data analysts, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are categorized into “Data Analysis and Applications” and “Methods and Concepts.” Appendices introduce R and Python and provide solutions for odd-numbered exercises.
Alan Agresti, Esteemed Professor Emeritus at the University of Florida, is the author of seven books, including Categorical Data Analysis (Wiley) and Statistics: The Art and Science of Learning from Data (Pearson), and has delivered brief courses in 35 countries. His accolades include an honorary doctorate from De Montfort University (UK) and Statistician of the Year from the American Statistical Association (Chicago chapter).
Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University, authored the monograph Contingency Table Analysis: Methods and Implementation Using R (Birkhäuser/Springer) and a textbook on mathematics for economists (in German). She possesses extensive experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, Business Administration, and Engineering.
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