Top 10 Free Resources To Learn R
R is a popular statistical language that provides high-level graphics for visualisations, debugging facilities, interfaces to other languages and more. According to Data Science Skills Study report 2020, languages like Python and R dominated the preference scale, with a combined value of 81.9% utilisation for statistical modelling. Here is a list of the top ten free resources to learn R. 1| Data Science: R Basics About: Data Science: R Basics helps understand the basics of R programming in data science. This course will help you learn the functions and data types of the R language, understand how to operate on vectors and when to use advanced functions like sorting etc. You will also learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyse and visualise data. Through this course, you will be able to develop a skill set comprising R programming, data wrangling with dplyr, data visualisation with ggplot2, file organisation with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio. Know more here. 2| Learn Data Science With R Part 1 of 10 About: “Learn Data Science With R Part 1 of 10” will help you learn the basics of data science, data types, vectors, factors, list matrices, data frames, among others. You will also understand how to read data from Oracle database using RJDBC, RODBC and ROracle. Know more here. 3| Data Science, Machine Learning, Data Analysis, Python & R About: In this course, you will learn new skills and understand the challenging yet lucrative sub-fields of data science including statistics, probability, data analysis, etc. The course includes practical exercises based on real-life examples. It includes both Python and R code templates which you can download and use for your projects. The topics covered include data visualisation using R Programming, data analysis using NumPy and Pandas, etc. Know more here. 4| R for Data Science About: This book by Hadley Wickham and Garrett Grolemund will help you understand how to do data science with R. You will learn how to get your data into the R language, how to transform it, visualise as well as model it. You will also learn how to utilise the grammar of graphics, literate programming, etc. Know more here. 5| R Programming for Data Science About: R Programming for Data Science is a book written by Roger D. Peng. The e-book will teach you the fundamentals of the R programming language. You will also learn the basics of the R language and the steps to manipulate datasets. The ebook will also assist you in understanding how to write various R functions, how to debug and optimise code, etc. With the fundamentals provided in this book, you will have a solid foundation on to build your data science toolbox. Know more here. 6| Hands-On Machine Learning With R About: The book “Hands-On Machine Learning with R” provides modules for many common machine learning methods, such as generalised low-rank models, clustering algorithms, regularised models, gradient boosting machines, among others. You will learn how to build as well as tune various models with R packages. This ebook is meant for users who  wish to learn how to use the machine learning stack within R. It also includes examples using various R packages such as glmnet, XGBoost, lime to effectively model and gain insight from data. Know more here. 7| Data Science: Foundations Using R Specialisation About: This covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. The students will complete a project at the end of each course in this specialisation. Projects include installing tools, programming in R, cleaning data, performing analyses, as well as peer review assignments. Know more here. 8| Statistics with R Specialisation About: You will learn to analyse and visualise data in R language as well as create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, among others. The course will also help you learn modelling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, in context without relying on statistical jargon, critique data-based claims and evaluate data-based decisions and more. Know more here. 9| Introduction to Data Science: Data Analysis and Prediction Algorithms with R About: The free ebook, written by Rafael A. Irizarry, introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers various concepts from statistical inference, linear regression, probability, and machine learning. It also helps you in developing skills such as data wrangling with dplyr, algorithm building with a caret, data visualisation with ggplot2, version control with Git and GitHub, among others. Know more here. 10| Data Visualisation & Dashboarding with R Specialisation About: This Specialisation is intended for people seeking to develop the ability to visualise data using R. The Specialisation includes five courses, which will help you learn R to create static and interactive data visualisations and publish them on the web. The course will also help you prepare to extract insights into various types of audiences. Know more here.
R is a popular statistical language that provides high-level graphics for visualisations, debugging facilities, interfaces to other languages and more. According to Data Science Skills Study report 2020, languages like Python and R dominated the preference scale, with a combined value of 81.9% utilisation for statistical modelling. Here is a list of the top ten […]
According to Data Science Skills Study report 2020, languages like Python and R dominated the preference scale, with a combined value of 81.9% utilisation for statistical modelling.
[]
["data science", "NumPy", "Go", "machine learning", "AI", "Python", "Aim", "XGBoost", "R", "Pandas"]
["AI", "machine learning", "data science", "Aim", "XGBoost", "Pandas", "NumPy", "Python", "R", "Go"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.