What is R programming?

R programming is the use of the R computer language for statistical analysis and graphic presentation. R is commonly used in business and research computing environments to analyze and visualize data, then create reports that can be used for decision making. R programming is increasingly more important given the expansion of big data for analysis.

Why learn R programming?

It's important to learn R programming if you want to be able to build computer programs that wrangle data and convert it into usable information. Organizations often have large amounts of data but are unable to understand what it means. Using programs written by R, you can generate Bayesian statistics and graphic analysis for business analytics, public health, and medical research, among other industries. Learning R is a component of learning data science, so another reason to study R programming is to get some of the fundamentals completed before venturing deeper into computer science studies.

What are typical careers that use R programming?

Typical careers that use R programming are in business analytics, financial services, and medical research. It is also a skill used in many data science roles. R programming pulls out information from large sets of data, so any field that calls for statistical inference from big data needs competent R programmers to create the analytics and reports needed. Some experience with R programming is useful for people who will be managing programming teams or requesting reports made from programs written in R. As big data analysis becomes more important in more fields, R programming becomes more valuable in the workplace.

How can online courses help me learn R programming?

Online courses can help you learn R programming by introducing the fundamentals of the language, teaching how it connects to such industries as finance and health care, and offering projects that let you show what you have learned. Courses are offered at all levels, from beginning to advanced. Many of them set you up for further work in data science or allow you to earn a specialization or certificate.

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R Programming

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"r programming"에 대한 229개의 결과

Johns Hopkins University
Data Science: Foundations using R
획득할 기술: Application Development, Business Analysis, Computer Programming, Data Analysis, Data Clustering Algorithms, Data Management, Data Visualization, Exploratory Data Analysis, Extract, Transform, Load, Ggplot2, Knitr, Plot (Graphics), Probability & Statistics, R Programming, Software, Software Engineering Tools, Statistical Programming
4.6
(46.5k개의 검토)
Beginner · Specialization
Duke University
Data Analysis with R
획득할 기술: Analysis, Bayesian Statistics, Business Analysis, Data Analysis, Data Mining, Data Visualization, Econometrics, Experiment, Exploratory Data Analysis, General Statistics, Linearity, Machine Learning, Machine Learning Algorithms, Mathematics, Modeling, Plot (Graphics), Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Analysis, Statistical Programming, Statistical Tests
4.7
(6.5k개의 검토)
Beginner · Specialization
Google
Google Data Analytics
획득할 기술: Algorithms, Application Development, Big Data, Budget Management, Business Analysis, Business Communication, Change Management, Cloud Computing, Communication, Computational Logic, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Model, Data Structures, Data Type, Data Visualization, Data Visualization Software, Database Administration, Database Design, Databases, Decision Making, Design and Product, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, General Statistics, Ggplot2, Interactive Data Visualization, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Microsoft Excel, Network Security, Other Programming Languages, Plot (Graphics), Presentation, Probability & Statistics, Problem Solving, Product Design, Programming Principles, Project Management, R Programming, Research and Design, SQL, Security Engineering, Security Strategy, Small Data, Software, Software Engineering, Software Security, Spreadsheet Software, Statistical Analysis, Statistical Programming, Storytelling, Strategy and Operations, Theoretical Computer Science, Visual Design
4.8
(63.4k개의 검토)
Beginner · Professional Certificate
IBM
IBM Data Analytics with Excel and R
획득할 기술: Advertising, Analysis, Analytics, Apache, Big Data, Business Analysis, Communication, Computational Logic, Computer Programming, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Structures, Data Visualization, Data Visualization Software, Data Warehousing, Databases, Extract, Transform, Load, General Statistics, Leadership and Management, Marketing, Microsoft Excel, NoSQL, Operating Systems, Plot (Graphics), Probability & Statistics, Professional Development, Programming Principles, R Programming, Regression, SQL, Software, Spreadsheet Software, Statistical Analysis, Statistical Programming, Statistical Visualization, System Programming, Theoretical Computer Science
4.7
(9.7k개의 검토)
Beginner · Professional Certificate
IBM
Applied Data Science with R
획득할 기술: Advertising, Business Analysis, Communication, Computational Logic, Computer Programming, Data Analysis, Data Management, Data Visualization, Databases, Extract, Transform, Load, Marketing, Plot (Graphics), Probability & Statistics, Programming Principles, R Programming, Regression, SQL, Statistical Analysis, Statistical Programming, Theoretical Computer Science
4.4
(269개의 검토)
Beginner · Specialization

r programming과(와) 관련된 검색

advanced r programming

the r programming environment

introduction to r programming for data science

introduction to r programming and tidyverse

data analysis with r programming

application of data analysis in business with r programming

r-programmierung zur datenanalyse

요약하자면, 여기에 가장 인기 있는 r programming 강좌 10개가 있습니다.

Data Science: Foundations using R: Johns Hopkins University
R Programming: Johns Hopkins University
Data Analysis with R Programming: Google
Data Analysis with R: Duke University
Getting Started with R: Coursera Project Network
Google Data Analytics: Google
Data Visualization & Dashboarding with R: Johns Hopkins University
IBM Data Analytics with Excel and R: IBM
Data Analysis with Tidyverse: University of Colorado Boulder
Introduction to R Programming for Data Science: IBM

Data Analysis에서 학습할 수 있는 스킬

R 프로그래밍에 대한 자주 묻는 질문

It's important to learn R programming if you want to be able to build computer programs that wrangle data and convert it into usable information. Organizations often have large amounts of data but are unable to understand what it means. Using programs written by R, you can generate Bayesian statistics and graphic analysis for business analytics, public health, and medical research, among other industries. Learning R is a component of learning data science, so another reason to study R programming is to get some of the fundamentals completed before venturing deeper into computer science studies.‎
Typical careers that use R programming are in business analytics, financial services, and medical research. It is also a skill used in many data science roles. R programming pulls out information from large sets of data, so any field that calls for statistical inference from big data needs competent R programmers to create the analytics and reports needed. Some experience with R programming is useful for people who will be managing programming teams or requesting reports made from programs written in R. As big data analysis becomes more important in more fields, R programming becomes more valuable in the workplace.‎
Online courses can help you learn R programming by introducing the fundamentals of the language, teaching how it connects to such industries as finance and health care, and offering projects that let you show what you have learned. Courses are offered at all levels, from beginning to advanced. Many of them set you up for further work in data science or allow you to earn a specialization or certificate.‎