Data Science With R Programming Basics

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Data Science With R Programming Basics

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Author : Dr.Sudhakar.K
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-08-13

Data Science With R Programming Basics written by Dr.Sudhakar.K and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-13 with Computers categories.

Dr.Sudhakar.K, Associate Professor, Department of Artificial Intelligence & Data Science, NITTE Meenakshi Institute of Technology, Bangalore, Karnataka, India. Mrs.Geethanjali.S.G, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India. Mrs.Rashmi.D.M, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India. Mrs.Sinchana K.P, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India.

Data Science With R Programming Basics

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Author : Mrs.Rasmita Kumari Mohanty
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-03-25

Data Science With R Programming Basics written by Mrs.Rasmita Kumari Mohanty and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-25 with Computers categories.

Mrs.Rasmita Kumari Mohanty, Assistant Professor, Department of CSE-(CyS, DS) and AI & DS, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. Mr.A.Pramod Kumar, Assistant Professor, Department of (CSE-CyS, DS) and AI & DS, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. Dr.Yuvaraju Macha, Associate Professor, Department of Mathematics, Matrusri Engineering College, Saidabad, Hyderabad, Telangana, India. R.Madhavi, Assistant Professor, Deparment of Mathematics, Matrusri Engineering College, Saidabad, Hyderabad, India.

R Programming For Data Science

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Author : Roger D. Peng
language : en
Publisher:
Release Date : 2012-04-19

R Programming For Data Science written by Roger D. Peng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-19 with R (Computer program language) categories.

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

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.

Basic Business Analytics Using R

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Author : Dr. Mahavir M. Shetiya
language : en
Publisher: Thakur Publication Private Limited
Release Date : 2023-11-10

Basic Business Analytics Using R written by Dr. Mahavir M. Shetiya and has been published by Thakur Publication Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-10 with Education categories.

Buy BASIC BUSINESS ANALYTICS USING R e-Book for Mba 2nd Semester in English language specially designed for SPPU ( Savitribai Phule Pune University ,Maharashtra) By Thakur publication.

Understanding Applying Basic Statistical Methods Using R

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Author : Morgan Holland &
language : en
Publisher: Scientific e-Resources
Release Date : 2019-07-04

Understanding Applying Basic Statistical Methods Using R written by Morgan Holland & and has been published by Scientific e-Resources this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-04 with categories.

Understanding and Applying Basic Statistical Methods Using R remarkably conquers any hindrance between propels in the measurable writing and methods routinely utilized by non-analysts. Giving a theoretical premise to understanding the relative benefits and uses of these methods, the book highlights current bits of knowledge and advances applicable to fundamental systems regarding managing non-ordinariness, exceptions, heteroscedasticity (unequal changes), and curvature. Including a manual for R, the book utilizes R programming to investigate starting factual ideas and standard methods for managing known issues related with exemplary procedures. Altogether classroom tried, the book incorporates segments that attention on either R programming or computational points of interest to enable the reader to wind up noticeably familiar with fundamental ideas and standards basic regarding understanding and applying the numerous methods as of now accessible.

R Programming For Data Analytics

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Author : Dr.S.Rasheed Mansoor Ali
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-06-23

R Programming For Data Analytics written by Dr.S.Rasheed Mansoor Ali and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-23 with Computers categories.

Dr.S.Rasheed Mansoor Ali, Assistant Professor, Department of Computer Applications, Jamal Mohamed College (Autonomous), Tiruchirappalli, Tamil Nadu, India. Dr.N.Prakash, Assistant Professor, Department of Management Studies, The American College, Madurai, Tamil Nadu, India. Dr.S.Sivagami, Associate Professor, Department of Computer Science & Engineering , Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India. Dr.T.Gunasekar, Professor, Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Avadi, Chennai, Tamil Nadu, India. Dr.G.Stephen, Assistant Librarian, St. Xavier's University, Kolkata, West Bengal.

R Programming

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Author : Andy Vickler
language : en
Publisher: R Programming
Release Date : 2022-04-28

R Programming written by Andy Vickler and has been published by R Programming this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-28 with categories.

R is an open-source statistical environment and programming language that has grown in popularity for data management and analysis in various industries. "R" Programming teaches you all the R you'll ever need in a rapid and painless manner. This accessible tutorial taught you your way around a list with no previous programming expertise and loads of practical examples, step-by-step exercises, and sample code. This book covers the most significant modeling and prediction methods, as well as their applications. Learn how to use R to transform raw data into knowledge, understanding, and insight. This book introduces you to R, RStudio, and the tidyverse, a set of R tools that work together to make data research simple, fluent, and enjoyable. This book is meant to get you practicing data science as fast as possible, even if you have no prior programming expertise. You'll get a comprehensive grasp of the data science cycle and the fundamental tools you'll need to handle the details. R is becoming more well-known by the day, as large institutions embrace it as a standard. Its popularity stems partly from the fact that it is a free tool replacing expensive statistical software products that may take an undue amount of time to master. Furthermore, R allows a user to do complicated statistical analyses with only a few keystrokes, making advanced studies accessible and clear to a broad audience. Learn how to import data, construct and dismantle data objects, traverse R's environment system, develop your own functions, and utilize all of R's programming tools with this book. This book will not only teach you how to program but also how to use R for more than simply displaying and analyzing data. Most of the chapters are written for you to understand statistical data, so if you are a student, this book can guarantee to teach you some basic statistics that will help you get good grades. Interested in statistical computing ? R Programming: Data Analysis and Statistics is a beginner-friendly book. It is written in an accessible way, and deal with the basics as well as more complex problems. No prior statistical knowledge is required. This book may also help more advanced programmers expand their skills. This book is like a friend who advises and guides you on how to use the ggplot2 package for making data visualizations. With this book, you will learn how to get started with data visualization in R. You will learn basic concepts of graphics, and you will also learn how to perform statistical analyses. This book is for everyone who faces a difficult task when trying to make data visualizations using R. It is also for people who are interested in learning more about statistics and graphical techniques. The book also teaches readers how to get started with ggplot2, and it also introduces the basics of R so that readers are aware of the basic commands and functions in R, as well as importing libraries. R has transformed the way statistical modeling approaches are applied, and has become a tool which is commonly used by analysts, programmers and visual artists alike. Its ease of use and flexibility make it an invaluable tool for working with large amounts of data.

Data Science Concepts And Techniques With Applications

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Author : Usman Qamar
language : en
Publisher: Springer Nature
Release Date : 2023-04-02

Data Science Concepts And Techniques With Applications written by Usman Qamar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-02 with Computers categories.

This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

Introduction To Biomedical Data Science

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Author : Robert Hoyt
language : en
Publisher: Lulu.com
Release Date : 2019-11-24

Introduction To Biomedical Data Science written by Robert Hoyt and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-24 with Science categories.

Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises.