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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IBM
Introduction to Data Science
Kompetenzen, die Sie erwerben: Communication, Computer Programming, Data Analysis, Data Management, Data Mining, Database Administration, Database Application, Databases, General Statistics, Machine Learning, Marketing, Probability & Statistics, Python Programming, R Programming, Regression, SPSS, SQL, Statistical Programming
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IBM
Data Science Fundamentals with Python and SQL
Kompetenzen, die Sie erwerben: Basic Descriptive Statistics, Computational Logic, Computer Programming, Data Analysis, Data Management, Data Visualization, Database Administration, Database Application, Databases, Extract, Transform, Load, General Statistics, Hypothesis, Hypothesis Testing, Machine Learning, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Python Programming, R Programming, SPSS, SQL, Statistical Hypothesis Testing, Statistical Programming, Theoretical Computer Science, Web
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Johns Hopkins University
Genomic Data Science
Kompetenzen, die Sie erwerben: Algebra, Algorithms, Big Data, Bioinformatics, Business Analysis, Command-Line Interface, Communication, Computer Programming, Computer Programming Tools, Data Analysis, Data Management, Experiment, General Statistics, Marketing, Mathematics, Other Programming Languages, Probability & Statistics, Python Programming, R Programming, Statistical Programming, Theoretical Computer Science
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University of California, Santa Cruz
Bayesian Statistics
Kompetenzen, die Sie erwerben: Bayesian, Bayesian Statistics, Econometrics, Forecasting, General Statistics, Graph Theory, Machine Learning, Markov Model, Mathematics, Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Machine Learning, Statistical Programming, Theoretical Computer Science
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University of Illinois at Urbana-Champaign
Business Data Management and Communication
Kompetenzen, die Sie erwerben: Accounting, Accounts Payable and Receivable, Advertising, Behavioral Economics, Big Data, Business Analysis, Business Intelligence, Business Psychology, Communication, Data Management, Data Visualization, Database Administration, Databases, Entrepreneurship, Finance, Financial Accounting, General Accounting, Generally Accepted Accounting Principles (GAAP), Leadership and Management, Marketing, R Programming, Sales, Security Engineering, Social Media, Software Security, Statistical Programming, Strategy and Operations, Supply Chain Systems, Supply Chain and Logistics, Taxes
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Duke University
Entrepreneurial Finance: Strategy and Innovation
Kompetenzen, die Sie erwerben: Accounting, Algorithms, Bayesian Statistics, BlockChain, Business Analysis, Corporate Accouting, Cryptography, Data Analysis, Data Management, Data Structures, Digital Marketing, Econometrics, Entrepreneurial Finance, Entrepreneurship, FinTech, Finance, Financial Analysis, Investment Management, Marketing, Probability & Statistics, R Programming, Risk, Risk Management, Security Engineering, Statistical Programming, Statistical Tests, Theoretical Computer Science
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(1.1k Bewertungen)
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Johns Hopkins University
Neuroscience and Neuroimaging
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Zusammenfassend sind hier 10 unserer beliebtesten r programming Kurse

Introduction to R: Basic R syntax: Coursera Project Network
Get Started with R Markdown: Coursera Project Network
Build Data Analysis and Transformation Skills in R using DPLYR: Coursera Project Network
Tools for Data Science: IBM
Introduction to Data Science: IBM
Data Science Fundamentals with Python and SQL: IBM
Genomic Data Science: Johns Hopkins University
Methods and Statistics in Social Sciences: University of Amsterdam
Bayesian Statistics: University of California, Santa Cruz
Business Data Management and Communication: University of Illinois at Urbana-Champaign

Fähigkeiten, die Sie bei Data Analysis erlernen können

Analytik (85)

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Python-Programmierung (47)

Unternehmensanalytik (40)

R-Programmierung (37)

Statistische Analyse (36)

SQL (33)

Datenmodell (29)

Data-Mining (27)

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Häufig gestellte Fragen zum Thema R-Programmierung

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.‎

Diese häufig gestellten Fragen dienen nur zu Informationszwecken. Den Lernenden wird empfohlen, eingehender zu recherchieren, ob Kurse und andere angestrebte Qualifikationen wirklich ihren persönlichen, beruflichen und finanziellen Vorstellungen entsprechen.