عنوان

Foundations of statistics for data scientists

شابک

9781000462937

شابک

9781003159834

شابک

9780367748456

شماره

E3721

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Foundations of statistics for data scientists

ساير اطلاعات عنواني

with R and Python

نام نخستين پديدآور

Alan Agresti and Maria Kateri.

محل نشرو پخش و غیره

Boca Raton

نام ناشر، پخش کننده و غيره

CRC Press

تاریخ نشرو بخش و غیره

2021.

نام خاص و کميت اثر

xvii, 467 p.

عنوان فروست

(Chapman & Hall/CRC texts in statistical science)

متن يادداشت

Includes bibliographical references and index.

متن يادداشت

"Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python 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 modelling. 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. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, 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 grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises. 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 the 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 (Birkh�auser/Springer) and a textbook on mathematics for economists (in German). She has a long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, and Business Administration and Engineering. "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...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 aspects of this manuscript that I find appealing: 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)"--

عنصر شناسه ای

Mathematical analysis

عنصر شناسه ای

Quantitative research

عنصر شناسه ای

R (Computer program language)

عنصر شناسه ای

Python (Computer program language)

تقسیم فرعی موضوعی

Statistical methods.

تقسیم فرعی موضوعی

Statistical methods.

ويراست

شماره رده

عنصر شناسه اي

Agresti, Alan,

عنصر شناسه اي

Kateri, Maria,

کشور

ایران

سازمان

University of Tehran. Library of College of Science

تاريخ عمليات

20220601164811.0

قواعد فهرست نويسي ( بخش توصيفي )

rda

تاريخ و ساعت مذاکره و دسترسي

UT_SCI_BL_DB_1004010_0001.pdf

فرمت انتشار

e

نوع ماده

BL

کد کاربرگه

278840

سطح دسترسي

a

تكميل شده

Y