Learn complete R programming from scratch for Data Science With Real Exercises, best R certification courses online from top online learning platforms › Udemy | › Coursera | › edx | › Linkedin learning | › Udacity | › Alison .
** Learn R programming for free or at low cost .
** Gain Certificate of completion.
Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2. Learn about integer, double, logical, character and other types in R.
Learn to program in R at a good level. Learn the core principles of programming. Learn how to create variables. Learn how to create a while() loop and a for() loop in R.
Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2.
Perform Data Preparation in R. Locate missing data in your dataframes. Apply the Factual Analysis method to replace missing records. Know how to reset the dataframe index.
Learn how to use the R programming language for data science and machine learning and data visualization.
Program in R. Create Data Visualizations. Use R to manipulate data easily. Use R for Data Analysis. Use R to handle csv,excel,SQL files or web scraping.
Learn the fundamentals of programming in R. Build your own functions in R. Learn the core tools for data science with R. Systematically explore data in R.
Work with R’s conditional statements, functions, and loops. Get your data in and out of R. Manipulate data with the Tidyverse ecosystem of packages.
Learn the basics of writing code in R - your first step to become a data scientist. Work with vectors, matrices and lists.
Manage data frames. Build their own functions and binary operations. Create charts in base R. Write complex programming structures (loops and conditional statements).
Learn how to solve real life problem using the Machine learning techniques. Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc..
Understanding of basics of statistics and concepts of Machine Learning. How to do basic statistical operations and run ML models in R.
Learn to program in R at a good level and how to use R Studio. Learn how to create vectors in R. Learn Data types in R.
Learn how to build and use matrices in R. Learn to use Factors in R. Learn how to install packages in R. Learn the core principles of R programming.
Gain new knowledge about R programming you wouldn't intuitively imagine - Extensive use of the tidyverse packages.
Learn How to do file management operation like copying and pasting files, deleting them, creating new directories and more and combining these operations with loops. How to loop with the maps family of functions.
You will learn to Use R to clean, analyze, and visualize data. Use GitHub to manage data science projects.
Learn how to ask the right questions, obtain data, and perform reproducible research. Set up R, R-Studio, Github and other useful tools.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts.
Understand critical programming language concepts. Make use of R loop functions and debugging tools. Configure statistical programming software.
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions.
Introduction to Probability and Data with R. Inferential Statistics. Linear Regression and Modeling. Bayesian Statistics.
This Specialization is intended for learners seeking to develop the ability to visualize data using R.
Getting Started with Data Visualization in R. Data Visualization in R with ggplot2. Advanced Data Visualization with R. Publishing Visualizations in R with Shiny and flexdashboard.
Manipulate numeric and textual data types using the R programming language and RStudio or Jupyter Notebooks. Control program flow, define functions, perform character string and date operations, define regular expressions, and handle errors.
Define and manipulate R data structures, including vectors, factors, lists, and data frames. Read, write, and save data files and scrape web pages using R.
You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges.
Real-world case studies to jumpstart your career. Fundamental R programming skills.
Statistical concepts such as probability, inference, and modeling and how to apply them in practice. Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr.
This course covers the basics of R: a free programming language and software environment used for statistical computing and graphics.
This course covers an introduction to R, from installation to basic statistical functions.
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
Build a foundation in R and learn how to wrangle, analyze, and visualize data. How to perform operations in R including sorting, data wrangling using dplyr, and making plots.
Foundational R programming concepts such as data types, vectors arithmetic, and indexing.
The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend R’s power. You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis.
This training series provides a thorough introduction to R, with detailed instruction for installing and navigating R and RStudio and hands-on examples, from exploratory graphics to neural networks.
Learn how to create visualizations such as bar charts, histograms, and scatterplots and transform categorical, qualitative, and outlier data to best meet your research questions and the requirements of your algorithms.
Review R programming language basics, discover methods to improve existing R code, explore new and interesting features, and learn about useful development tools and libraries that will make your time programming with R that much more productive.
Learn how to work with ggplot2 to create basic visualizations, how to beautify those visualizations by applying different aesthetics, and how to visualize data with maps.
This course shows why R is ideal for high volumes of data, introduces more efficient ways to use the language, and explains how to avoid the problems and capitalize on the opportunities of big data.
Learn the programming fundamentals required for a career in data science. By the end of the program, you will be able to use R, SQL, Command Line, and Git.
Learn R programming fundamentals such as data structures, variables, loops, and functions. Learn to visualize data in the popular data visualization library ggplot2.
Learn the programming fundamentals required for a career in data science. By the end of the program, you will be able to use Python, SQL, Command Line, and Git.
Learn Python programming fundamentals such as data structures, variables, loops, and functions. Learn to work with data using libraries like NumPy and Pandas.
Students will get an introduction to the R coding language with this free online introduction to R course.
Learn about Linear & Logistic Regression, Decision Trees & SVM in R programming with this Machine Learning in R course.
Learn more about R programming techniques and the consumer wants approach in marketing from this free online course.
R is a programming language and free software and it possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational tasks, C, C++ and Fortran codes are preferred.