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R For Data Science

필터링 기준

"r for data science"에 대한 221개의 결과

Johns Hopkins University
Data Science: Foundations using R
획득할 기술: Analysis, 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 Engineering Tools, Statistical Programming
4.6
(46.5k개의 검토)
Beginner · Specialization
Duke University
Data Analysis with R
획득할 기술: Bayesian Statistics, Business Analysis, Data Analysis, Data Mining, Data Visualization, Econometrics, Experiment, Exploratory Data Analysis, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematics, Modeling, Plot (Graphics), Probability, Probability & Statistics, Probability Distribution, R Programming, Regression, Regression Analysis, Statistical Analysis, Statistical Programming, Statistical Tests
4.7
(6.6k개의 검토)
Beginner · Specialization
IBM
IBM Data Analytics with Excel and R
획득할 기술: Advertising, Analysis, 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
(9.8k개의 검토)
Beginner · Professional Certificate
Johns Hopkins University
Tidyverse Skills for Data Science in R
획득할 기술: Advertising, Business Analysis, Communication, Computational Logic, Computer Programming, Data Analysis, Data Management, Data Mining, Data Structures, Data Visualization, Databases, Exploratory Data Analysis, Extract, Transform, Load, Machine Learning, Marketing, Mathematics, Natural Language Processing, Plot (Graphics), Probability & Statistics, Programming Principles, R Programming, Regression, Software Architecture, Software Engineering, Spreadsheet Software, Statistical Programming, Statistical Tests, Theoretical Computer Science
4.4
(65개의 검토)
Beginner · Specialization
무료
Stanford University
Introduction to Statistics
획득할 기술: Data Analysis, Regression, Hypothesis, Econometrics, Basic Descriptive Statistics, Machine Learning, Markov Model, Correlation And Dependence, Inference, Statistical Tests, Probability & Statistics, Bayesian Statistics, Probability, Hypothesis Testing, Statistical Inference, Experiment, General Statistics, Analysis, Statistical Analysis, Probability Distribution
4.5
(1.1k개의 검토)
Beginner · Course

r for data science과(와) 관련된 검색

introduction to r programming for data science

sql for data science with r

tidyverse skills for data science in r

요약하자면, 여기에 가장 인기 있는 r for data science 강좌 10개가 있습니다.

Data Science: Foundations using R: Johns Hopkins University
Data Analysis with R: Duke University
Data Analysis with R Programming: Google
IBM Data Analytics with Excel and R: IBM
The R Programming Environment: Johns Hopkins University
Tidyverse Skills for Data Science in R: Johns Hopkins University
Data Analysis with R: IBM
Introduction to R Programming for Data Science: IBM
Introduction to Probability and Data with R: Duke University
Introduction to Statistics: Stanford University

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