Statistical Application Development With R And Python

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Statistical Application Development with R and Python - Second Edition

Author : Prabhanjan Narayanachar Tattar
Publisher :
ISBN 13 : 9781788621199
Total Pages : 432 pages
Book Rating : 4.6/5 (211 download)

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Book Synopsis Statistical Application Development with R and Python - Second Edition by : Prabhanjan Narayanachar Tattar

Download or read book Statistical Application Development with R and Python - Second Edition written by Prabhanjan Narayanachar Tattar and published by . This book was released on 2017-08-30 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Software Implementation Illustrated with R and PythonAbout This Book* Learn the nature of data through software which takes the preliminary concepts right away using R and Python.* Understand data modeling and visualization to perform efficient statistical analysis with this guide.* Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.Who This Book Is ForIf you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versaWhat You Will Learn* Learn the nature of data through software with preliminary concepts right away in R* Read data from various sources and export the R output to other software* Perform effective data visualization with the nature of variables and rich alternative options* Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference* Learn statistical inference through simulation combining the classical inference and modern computational power* Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics* Introduce yourself to CART - a machine learning tool which is very useful when the data has an intrinsic nonlinearityIn DetailStatistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions.This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world.You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.Style and approachDeveloping better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning

Statistical Application Development with R and Python

Author : Prabhanjan Narayanachar Tattar
Publisher : Packt Publishing Ltd
ISBN 13 : 178862226X
Total Pages : 432 pages
Book Rating : 4.7/5 (886 download)

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Book Synopsis Statistical Application Development with R and Python by : Prabhanjan Narayanachar Tattar

Download or read book Statistical Application Development with R and Python written by Prabhanjan Narayanachar Tattar and published by Packt Publishing Ltd. This book was released on 2017-08-31 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Software Implementation Illustrated with R and Python About This Book Learn the nature of data through software which takes the preliminary concepts right away using R and Python. Understand data modeling and visualization to perform efficient statistical analysis with this guide. Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics. Who This Book Is For If you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa What You Will Learn Learn the nature of data through software with preliminary concepts right away in R Read data from various sources and export the R output to other software Perform effective data visualization with the nature of variables and rich alternative options Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference Learn statistical inference through simulation combining the classical inference and modern computational power Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity In Detail Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects. Style and approach Developing better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning

An Introduction to Statistical Learning

Author : Gareth James
Publisher : Springer Nature
ISBN 13 : 3031387473
Total Pages : 617 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

R Statistical Application Development by Example Beginner's Guide

Author : Prabhanjan Narayanachar Tattar
Publisher : Packt Pub Limited
ISBN 13 : 9781849519441
Total Pages : 344 pages
Book Rating : 4.5/5 (194 download)

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Book Synopsis R Statistical Application Development by Example Beginner's Guide by : Prabhanjan Narayanachar Tattar

Download or read book R Statistical Application Development by Example Beginner's Guide written by Prabhanjan Narayanachar Tattar and published by Packt Pub Limited. This book was released on 2013 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full of screenshots and examples, this Beginner's Guide by Example will teach you practically everything you need to know about R statistical application development from scratch. You will begin learning the first concepts of statistics in R which is vital in this fast paced era and it is also a bargain as you do not need to do a preliminary course on the subject.

Data Science Using Python and R

Author : Chantal D. Larose
Publisher : John Wiley & Sons
ISBN 13 : 1119526817
Total Pages : 256 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Data Science Using Python and R by : Chantal D. Larose

Download or read book Data Science Using Python and R written by Chantal D. Larose and published by John Wiley & Sons. This book was released on 2019-04-09 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

The Book of R

Author : Tilman M. Davies
Publisher : No Starch Press
ISBN 13 : 1593276516
Total Pages : 833 pages
Book Rating : 4.5/5 (932 download)

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Book Synopsis The Book of R by : Tilman M. Davies

Download or read book The Book of R written by Tilman M. Davies and published by No Starch Press. This book was released on 2016-07-16 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

Advanced R

Author : Hadley Wickham
Publisher : CRC Press
ISBN 13 : 1498759807
Total Pages : 669 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Advanced R by : Hadley Wickham

Download or read book Advanced R written by Hadley Wickham and published by CRC Press. This book was released on 2015-09-15 with total page 669 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.

Practical Data Science Cookbook

Author : Prabhanjan Tattar
Publisher : Packt Publishing Ltd
ISBN 13 : 178712326X
Total Pages : 428 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Practical Data Science Cookbook by : Prabhanjan Tattar

Download or read book Practical Data Science Cookbook written by Prabhanjan Tattar and published by Packt Publishing Ltd. This book was released on 2017-06-29 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization

Comparative Approaches to Using R and Python for Statistical Data Analysis

Author : Sarmento, Rui
Publisher : IGI Global
ISBN 13 : 1522519890
Total Pages : 215 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Comparative Approaches to Using R and Python for Statistical Data Analysis by : Sarmento, Rui

Download or read book Comparative Approaches to Using R and Python for Statistical Data Analysis written by Sarmento, Rui and published by IGI Global. This book was released on 2017-01-06 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.

R for Data Science

Author : Hadley Wickham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Practical Statistics for Data Scientists

Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 322 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various 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 programming language, 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

An Introduction to Statistical Learning

Author : Gareth James
Publisher : Springer Science & Business Media
ISBN 13 : 1461471389
Total Pages : 434 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Science & Business Media. This book was released on 2013-06-24 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Training Systems Using Python Statistical Modeling

Author : Curtis Miller
Publisher : Packt Publishing Ltd
ISBN 13 : 1838820647
Total Pages : 284 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Training Systems Using Python Statistical Modeling by : Curtis Miller

Download or read book Training Systems Using Python Statistical Modeling written by Curtis Miller and published by Packt Publishing Ltd. This book was released on 2019-05-20 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Python and statistical modeling techniques for building accurate predictive models Key FeaturesGet introduced to Python's rich suite of libraries for statistical modelingImplement regression, clustering and train neural networks from scratchIncludes real-world examples on training end-to-end machine learning systems in PythonBook Description Python's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics. You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them. By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics. What you will learnUnderstand the importance of statistical modelingLearn about the various Python packages for statistical analysisImplement algorithms such as Naive Bayes, random forests, and moreBuild predictive models from scratch using Python's scikit-learn libraryImplement regression analysis and clusteringLearn how to train a neural network in PythonWho this book is for If you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.

The Art of R Programming

Author : Norman Matloff
Publisher : No Starch Press
ISBN 13 : 1593273843
Total Pages : 404 pages
Book Rating : 4.5/5 (932 download)

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Book Synopsis The Art of R Programming by : Norman Matloff

Download or read book The Art of R Programming written by Norman Matloff and published by No Starch Press. This book was released on 2011-10-11 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: –Create artful graphs to visualize complex data sets and functions –Write more efficient code using parallel R and vectorization –Interface R with C/C++ and Python for increased speed or functionality –Find new R packages for text analysis, image manipulation, and more –Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

Python for R Users

Author : Ajay Ohri
Publisher : John Wiley & Sons
ISBN 13 : 1119126770
Total Pages : 371 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Python for R Users by : Ajay Ohri

Download or read book Python for R Users written by Ajay Ohri and published by John Wiley & Sons. This book was released on 2017-11-03 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learn R Programming in 24 Hours

Author : Alex Nordeen
Publisher : Guru99
ISBN 13 :
Total Pages : 349 pages
Book Rating : 4./5 ( download)

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Book Synopsis Learn R Programming in 24 Hours by : Alex Nordeen

Download or read book Learn R Programming in 24 Hours written by Alex Nordeen and published by Guru99. This book was released on 2020-10-31 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference. R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple. R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze. Table of Contents Chapter 1: What is R Programming Language? Introduction & Basics Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows Chapter 3: R Data Types, Arithmetic & Logical Operators with Example Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice Chapter 5: Factor in R: Categorical & Continuous Variables Chapter 6: R Data Frame: Create, Append, Select, Subset Chapter 7: List in R: Create, Select Elements with Example Chapter 8: R Sort a Data Frame using Order() Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread) Chapter 10: Merge Data Frames in R: Full and Partial Match Chapter 11: Functions in R Programming (with Example) Chapter 12: IF, ELSE, ELSE IF Statement in R Chapter 13: For Loop in R with Examples for List and Matrix Chapter 14: While Loop in R with Example Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example Chapter 20: R Aggregate Function: Summarise & Group_by() Example Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example Chapter 22: Scatter Plot in R using ggplot2 (with Example) Chapter 23: How to make Boxplot in R (with EXAMPLE) Chapter 24: Bar Chart & Histogram in R (with Example) Chapter 25: T Test in R: One Sample and Paired (with Example) Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples) Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example] Chapter 28: Decision Tree in R with Example Chapter 29: R Random Forest Tutorial with Example Chapter 30: Generalized Linear Model (GLM) in R with Example Chapter 31: K-means Clustering in R with Example Chapter 32: R Vs Python: What's the Difference? Chapter 33: SAS vs R: What's the Difference?

A Tour of Data Science

Author : Nailong Zhang
Publisher : CRC Press
ISBN 13 : 1000215199
Total Pages : 217 pages
Book Rating : 4.0/5 (2 download)

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Book Synopsis A Tour of Data Science by : Nailong Zhang

Download or read book A Tour of Data Science written by Nailong Zhang and published by CRC Press. This book was released on 2020-11-11 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.