R For Data Science
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R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-06-08
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-08 with Computers categories.
Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto
R For Data Science
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Author : Dan Toomey
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-12-24
R For Data Science written by Dan Toomey and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-24 with Computers categories.
If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
Practical Statistics For Data Scientists
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Author : Peter Bruce
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-04-10
Practical Statistics For Data Scientists written by Peter Bruce and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-10 with Computers categories.
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data
Beginning Data Science In R
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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2017-03-09
Beginning Data Science In R written by Thomas Mailund and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-09 with Computers categories.
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.
Using R For Data Analysis In Social Sciences
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Author : Quan Li
language : en
Publisher: Oxford University Press
Release Date : 2018-05-09
Using R For Data Analysis In Social Sciences written by Quan Li and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-09 with Political Science categories.
Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.
Foundations Of Statistics For Data Scientists
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Author : Alan Agresti
language : en
Publisher: CRC Press
Release Date : 2021-11-29
Foundations Of Statistics For Data Scientists written by Alan Agresti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Business & Economics categories.
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. 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 (http://stat4ds.rwth-aachen.de/) has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.
Statistical Inference Via Data Science
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Author : Chester Ismay
language : en
Publisher: CRC Press
Release Date : 2025-05-02
Statistical Inference Via Data Science written by Chester Ismay and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Mathematics categories.
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition offers a comprehensive guide to learning statistical inference with data science tools widely used in industry, academia, and government. The first part of this book introduces the tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the infer package, a suitable complement to the tidyverse. By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All of these tasks are performed by strongly emphasizing data visualization. Key Features in the Second Edition: Minimal Prerequisites: No prior calculus or coding experience is needed, making the content accessible to a wide audience. Real-World Data: Learn with real-world datasets, including all domestic flights leaving New York City in 2023, the Gapminder project, FiveThirtyEight.com data, and new datasets on health, global development, music, coffee quality, and geyser eruptions. Simulation-Based Inference: Statistical inference through simulation-based methods. Expanded Theoretical Discussions: Includes deeper coverage of theory-based approaches, their connection with simulation-based approaches, and a presentation of intuitive and formal aspects of these methods. Enhanced Use of the infer Package: Leverages the infer package for “tidy” and transparent statistical inference, enabling readers to construct confidence intervals and conduct hypothesis tests through multiple linear regression and beyond. Dynamic Online Resources: All code and output are embedded in the text, with additional interactive exercises, discussions, and solutions available online. Broadened Applications: Suitable for undergraduate and graduate courses, including statistics, data science, and courses emphasizing reproducible research. The first edition of the book has been used in so many different ways--for courses in statistical inference, statistical programming, business analytics, and data science for social policy, and by professionals in many other means. Ideal for those new to statistics or looking to deepen their knowledge, this edition provides a clear entry point into data science and modern statistical methods.
R For Data Analysis In Easy Steps 2nd Edition
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Author : Mike McGrath
language : en
Publisher: In Easy Steps Limited
Release Date : 2023-06-05
R For Data Analysis In Easy Steps 2nd Edition written by Mike McGrath and has been published by In Easy Steps Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-05 with Computers categories.
The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data Analysis in easy steps, 2nd edition has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. The book begins by explaining core programming principles of the R programming language, which stores data in “vectors” from which simple graphs can be plotted. Next, it describes how to create “matrices” to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create “data frames” from imported data sets, and how to employ the “Grammar of Graphics” to produce advanced visualizations that can best illustrate useful insights from your data. R for Data Analysis in easy steps, 2nd edition contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming. Updated for the latest version of R. Contents: Getting started Storing values Performing operations Testing conditions Employing functions Building matrices Constructing data frames Producing quick plots Telling stories with data Plotting perfection
Data Science And Analytics With Python R And Spss Programming
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Author : V.K. Jain
language : en
Publisher: KHANNA PUBLISHING HOUSE
Release Date :
Data Science And Analytics With Python R And Spss Programming written by V.K. Jain and has been published by KHANNA PUBLISHING HOUSE this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
The Book has been written completely as per AICTE recommended syllabus on "Data Sciences". SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science. With complete courseware for understand the key concepts in data science including their real-world applications and the toolkit used by data scientists. Implement data collection and management. Provided with state of the arts subjectwise. With all required tutorials on R, Python and Bokeh, Anaconda, IBM SPSS-21 and Matplotlib.
Python For R Users
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Author : Ajay Ohri
language : en
Publisher: John Wiley & Sons
Release Date : 2017-11-01
Python For R Users written by Ajay Ohri and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-01 with Computers categories.
The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.