For data analysis, data manipulation, graphics, statistical computation, and statistical data analysis, the R programming language is utilised. In a nutshell, R allows you to examine data sets in ways that go beyond what Excel can do.
Since its inception in the early 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand, the R programming language and development environment have risen in popularity.
Learn the major R data structures and how to use R code and R packages to produce amazing data visualisations. The course includes engaging, interactive coding tasks that reinforce your knowledge through practical applications.
This is the greatest R programming course on Udemy, and it was designed and delivered by Kirill Eremenko and his SuperDataScience team, who have built the top online data science and machine learning courses on Udemy.
You just need a high school maths level to finish this course. It's for anyone interested in R programming, Data Science, or Data Analytics, as well as anyone who isn't comfortable coding but is curious about Data Science and wants to apply it to datasets quickly.
Programming in R and RStudio, Data Analytics, Data Science, Statistical Analysis, how to construct and utilise metrics in R, dealing with statistical data in R, and comprehending the Normal distribution are all covered in this course.
You get lifetime access to 10.5 hours of video, 6 articles, and 38 digital materials when you buy it.(from)
This Udemy course provides a lot of materials and assistance for novices who want to master the foundations of programming in R. If you enjoy Star Wars or Pokémon, the lesson will be enjoyable for you because several examples are based on them.
How to deal with R's conditional statements, functions, and loops, getting your data in and out of R, understanding and doing regression analysis in R, learning the foundations of statistics and applying them in practise are all key takeaways from the course. methodically analyse data in R and create R functions
There are a total of 38 articles and 34 resources in the course's support documentation. All of this is covered in less than 7 hours of video.
The goal of the course is to teach students the fundamentals of R programming. The course begins with an overview of R's functions and data types before moving on to how to work with vectors and when to utilise advanced functions like sorting.
General programming concepts such as probability, inference, regression, and machine learning will also be addressed.
Finally, the course teaches skill sets such as dplyr data manipulation, ggplot2 data visualisation, UNIX/Linux file management, version control, and R studio repeatable document preparation.
This is a starting level course that will take around 8 weeks and 1-2 hours per week to complete.
List of R programming courses
This platform has created a list of training that will come in helpful if you want to improve on your R programming development abilities. It contains a total of 14 courses. There are lectures centred on R programming principles, data science using R, data visualisation, and more if you want to enhance your foundations.
Everyone, regardless of expertise level, may benefit from this series of classes. It works with different features and writes code to put the principles learned in the courses into practise. It may be used on a variety of platforms, including Microsoft Data Platform and RStudio, and it can also be used to create Spark applications utilising Cloudera, Python, and Scala.
This R language course will teach you how to programme in R, how to use R for data analysis, how to generate stunning data visualisations, and how to use R for machine learning.
Jose Portilla, one of the finest teachers on Udemy who has taught thousands of students about Data Science and Programming, created and teaches the course. The curriculum is designed for both experienced professionals who want to shift careers to data science and total beginners who want to learn all there is to know about data science and machine learning from the bottom up.
It works with csv, excel, SQL files, and web scraping using R. It also allows students to study machine learning methods such as linear regression and logistic regression, as well as more complex topics like decision trees, random forests, and support vector machines.
This R course includes over 100 HD video lectures, complete code notes for each lesson, eight articles, and three downloadable resources. There are 17.5 hours of on-demand video in the course.
This Nanodegree programme teaches learners the core data programming tools of R, SQL, command line, and git, preparing them for a future in data science. Introduction to SQL, Introduction to R Programming, and Introduction to Version Control are the three courses in this beginning programme. Learners perform three projects throughout the course of the curriculum, with an emphasis on the R programming language.
You'll learn how to utilise R data types and variables to represent and store data, as well as conditionals and loops to regulate programme execution. You'll also learn how to store groups of related data using sophisticated data structures such as lists. You'll also learn how to create custom functions, develop scripts, and deal with problems. The use of R libraries for data visualisation is also addressed in depth.
You can acquire personalised feedback on projects from a network of 900+ project reviewers are all highlights of the course. The programme will last three months, with each week requiring 10 hours of work.(from)
Instructor Barton Poulson introduces you to the statistical processing language R in this course. Before you begin the training, you need to first install R on your computer. Reading data from SPSS and spreadsheets, as well as employing packages for advanced R functions, are all covered in the lectures.
Learn how to make charts and graphs, as well as how to verify statistical assumptions and data dependability. The courses will conclude with lectures on how to convey data insights via presentations, web pages, and graphs.
From the introductions through the management of the final results, the films will walk you through all of the essential ideas. The training is broken down into six sections, each with its own set of exercises. The lectures provide a step-by-step guide on getting started with the exercises. Exercises are accessible for both online and offline practise via a download option. The course will last for 2 hours and 25 minutes.
In this course, Bogdan Anastasiei, who teaches quantitative techniques for business at the University of Iasi in Romania, teaches fundamental statistical analyses using the R software. With over 20 years of teaching experience, he has a wealth of knowledge that goes beyond conventional training.
The software teaches data manipulation in R (filtering and sorting data sets, recoding and computing variables), calculating skewness and kurtosis, and creating histograms and cumulative frequency charts. For individuals who are short on time, the crash course is 3 hours long and includes 13 additional materials, 12 articles, and complete lifetime access.
This course introduces a learner to the fundamentals of R programming. Aside from the fundamentals, you'll learn how to organise, alter, and clean data frames in R. In addition, a student will learn how to develop data visualisations in R that display data insights.
Data frames, data cleansing, basics of data visualisations, aggregates, joining tables, me, quartiles, quantiles, and interquartile, and hypothesis testing are all covered in the Learn R course.
To take this course, you do not need to know how to code. It takes 20 hours to complete, and upon completion, you will earn a certificate.
R for beginners introduces the fundamentals of the programming language and shows how to construct one's first R script and R project folder. It will assist a learner in distinguishing between data structures, manipulating data structures using built-in functions, reshaping, accessing elements, and converting R objects.
Building scatterplots, line charts, histograms, box plots, bar charts, mosaic plots, alternating default graphical settings, and exporting a figure from R will all be covered in this course.
The course is broken down into eight sections with a total of 79 lectures, and it takes around 14 hours to finish. It is presented by Marko Intihar, Data Scientist and Researcher, and requires no prior expertise.
(Must read: Descriptive Statistics in R)
To conclude the blog, these courses have proven to be very beneficial for beginners as a great learning milestone. You should know what your priority is in the field of R programming and choose the best course for you.
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