1. Introduction to Statistical Science 2. Probability Distributions 3. Sampling Distributions 4. Statistical Inference: Estimation Skip Product Menu 5. Statistical Inference: Significance Testing 6. Linear Models and Least Squares 7. Generalized Linear Models 8. Classification and Clustering 9. Statistical Science: A Historical Overview Appendices
Alan Agresti, Distinguished 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 presented short courses in 35 countries. His awards 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 has long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, Business Administration, and Engineering.
"[...] Overall, I found the book to be a creative and refreshing
take on the challenge of building foundations of “classical”
statistics while helping introduce newer topics that are
increasingly central to the statistical sciences. Important ideas
of the past 50 years (see Gelman and Ahtari 2021) such as
resampling, regularization, and hierarchical modeling are
incorporated as optional sections (marked with an asterisk). The
authors have captured much of the excitement of the statistical
sciences and shared it in a way that I believe that students (and
instructors) will share their enthusiasm. I look forward to
teaching using this book."
-Nicholas J. Horton in the Journal of the American Statistical
Association, July 2022"If you find the other books (co-)authored
by A. Agresti interesting, you will not be disappointed this time
either. The book is a very good mixture of theory and practice. It
presents the topics in statistical science that any data scientist
should be familiar with. ... In general, the theory is provided in
an easy to read and understand way. Mathematical details are
limited to minimum. The emphasis is on the intuitive explanation of
the statistical theory and its implementation in practice. And
because of that the theory is broadly illustrated with examples
based on the real data (which is an additional asset of the book).
... Another pro worth mentioning is the way how the book is
organized. It is extremely easy to go back and find the content
which is needed. Blueshaded areas with key messages, R codes
presented in blue, summaries at the end of each sections – all of
this makes this book very transparent and well organized. The book
can be truly recommended to students who would like to start their
journey as Data Scientists or young practitioners in this field. It
can be also a great inspiration for lecturers."
-Kinga Sałapa in ISCB Book Reviews, September 2022"The main goal
of this textbook is to present foundational statistical methods and
theory that are relevant in the field of data science. The authors
depart from the typical approaches taken by many conventional
mathematical statistics textbooks by placing more emphasis on
providing the students with intuitive and practical interpretations
of those methods with the aid of R programming codes. The book also
takes slightly different organizations and presents a few topics
that are not commonly found in conventional mathematical statistics
textbooks. Notably, the book introduces both the frequentist
approach and the Bayesian approach for each chapter on statistical
inference in Chapters 4 – 6...I find its particular strength to be
its intuitive presentation of statistical theory and methods
without getting bogged down in mathematical details that are
perhaps less useful to the practitioners."
-Mintaek Lee, Boise State University"The statistical training for
budding data scientists is different than the statistical training
for budding statisticians, or other scientists. Data scientists
require a different mix of theory and practice than statisticians,
plus a great deal more exposure to computation than many other
types of scientists. The aspects of this manuscript that I find
appealing for the courses I teach: 1. The use of real data. 2. The
use of R but with the option to use Python. 3. A good mix of theory
and practice. 4. The text is well-written with good exercises. 5.
The coverage of topics (e.g. Bayesian methods and clustering) that
are not usually part of a course in statistics at the level of this
book".
-Jason M. Graham, University of Scranton"This book distinguishes
itself with its focus on computational aspects of statistics (the
appendices on R and Python and the examples throughout the text
that use R). The ‘cost’ of this approach seems to be that much less
attention is given to probability than in a standard text. There is
a definite market for this approach – computational statistics/data
science do not really require as much probability background as is
usually given, while more focus on the way that things are actually
done in practice (with software such as R or Python) is extremely
beneficial to students that are looking to apply statistical
methods. There is a wealth of problems in the book, and their
variety (both computational and theoretical) is much appreciated.
Also, the expansive appendices on R and Python wonderful, and will
be of great help to students…Two major reasons that I would adopt
the book are that its discussions seem to be slightly
nontraditional in some cases (see above), yet still getting the
salient points across. I also am happy about the examples
throughout the text that use R–this is very useful for my
students."
-Christopher Gaffney, Drexel University"I will most likely adopt
the proposed book for my class. The book seems to provide just
about right level of mathematics—not too theoretical or like many
other cookbooks which are available for R programming."
-Tumulesh Solanky, University of New Orleans"The book is
well-written and the examples are well-suited for building
foundations for statistical science for data science as a
discipline. The material covers most of the theoretical backgrounds
in statistics. Throughout the book, the authors have used R
programming to illustrate the concepts. In many cases, simulations
were presented to support the theory. Each chapter has abundant
practical exercises for the readers to explore the materials
further. This textbook can serve as a textbook for a data science
curriculum."
-Steve Chung, Cal State University Fresno
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