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From zero to expert! So you can master R to program with this FREE ONLINE course – Teach me about Science

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If you've ever been curious to understand how data is used to make decisions in fields such as artificial intelligence, modeling financial the analysis of datalearn R can be your first step.

What is R and why is it important?

R is a language code programming open specially designed for statistical analysis and data visualization. It has been converted preferred choice for data scientists, analysts and statisticians due to its power and flexibility.

Because of this versatility and easy handling, the platform for specialized courses DataCamplaunches the initiative “Introduction to R”, a program of 6 main topics that will allow you to learn the programming language from scratch. Next, we present the complete syllabus that you can take at any time you want.


1.-Introduction to the fundamentals of R

In this first chapter, students become familiar with the basic operations of R. They learn to use the console as a calculator, assign variables, and understand the fundamental data types in R. This stage is essential to establish a solid foundation before diving into in more complex concepts.


The second topic focuses on vectors, which are ordered sets of data. Through a practical example based on gambling in Las Vegas, students learn to create vectors, name them, select specific elements of them, and compare different vectors. Vectors are one of the basic elements in R and are important for performing simple and advanced operations on data sets.


An array is a two-dimensional structure that contains elements of the same type. In this chapter, students learn how to create matrices in R and perform basic calculations with them. Using box office data for Star Wars films as an example, students gain skills in manipulating and analyzing data organized in matrices.

4.-Data Frames

Data frames are tabular data structures that allow heterogeneous data sets to be stored. In this topic, students learn how to create data frames, select interesting parts of a data frame, and sort a data frame according to specific variables. Since most data sets that data professionals will work with are stored as data frames, this skill is crucial for practical data analysis.


Factors are used to handle categorical or “factor” type data. In this chapter, students learn how to create, subset, and compare factors. The example of human hair color categorization is used to illustrate how R handles and analyzes categorized data, which is essential for understanding and working with data sets that contain non-numerical information.

SOURCE: Computerworld


Unlike vectors, lists in R can contain components of different types. In this final chapter, students learn how to create, name, and select subsets of lists. This theme allows them to handle more complex and heterogeneous data sets, preparing them for more advanced data analysis tasks.

After the course

By the end of this 4-hour course, you will have the ability to use R for your own data analysis. These skills are highly in demand in today's job market and can open doors in various professional areas, from data analyst to data scientist.

Link to the course: Sign up and access the curso “Introduction to R” of the DataCamp platform.

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