Technology today is based on data. Today’s highly digital world wants the devices that are smarter with data, their management, storage, and security features. The digital era allows you to store everything on your gadgets and you need not remember your account numbers, contact numbers and even birthdays. For everything, there are smart devices that work on data provided by you.
Data science has made all this possible. Statistical analysis and creating data visualization is done through a tool called R programming. Many industry giants use this programming language like Uber, Airbnb, Facebook, Google and many more. R is primarily used for statistics, visualization, and machine learning.
According to analysis, R programming has shown remarkable growth over the years. R has been the first choice in sectors like healthcare, government sector and consulting. So, it makes sense to achieve R programming certification and master this valuable skill.
What is R?
R is a programming language that was developed in 1993 by two professors of Auckland University, Ross Ihaka, and Robert Gentleman. This language includes machine learning algorithms, time series, linear regression, statistical inference, and many more features. Apart from the libraries written in R, the computational tasks are written in C++, C, and Fortran. An extensive catalog of statistical and graphical methods is also included in R.
There is a series of steps involved in data analysis using R, those are:
- Modeling and
R is a programming tool that is clear and accessible. There are effective data storage and management facilities and operators so as to perform calculations on matrices. There is a collection of libraries specially designed for data science. Discovering includes data investigation, refining hypothesis and analyzing them. There is a wide variety of tools that let you decide which model for your data. Codes, graphs, and outputs are then integrated into a report with R Markdown or build Shiny apps to share so that it can be shared.
R is specifically used for statistical inference, data analysis, and machine learning.
With the exponential increase in the need of using Data Science, every company relies on it for deciding the course of action. There is a wide variety of tools available in the market for analyzing the data. When you see the statistics of languages used in data science, you will find out that R is the most appropriate tool in the market. The biggest feature of R is that it runs on all platforms. Tech giants use this language for data analysts.
There are multiple ways in R that can be used to present and share work, which is a markdown document or a Shiny app. You can then host it in Rpub, Github or the website of your business. This implies that communication in R is very easy as compared to other programming languages. To write a document, R accepts markdown. The documents can further be exported in different formats. A document can be exported in HTML, Word and PDF/Latex. On the other hand, a presentation can be exported in HTML and PDF Beamer.
The primary reasons you should learn R are:
- Trends: R has been the most popular language to be learned since 2008. So, R programmers are in high demand since then.
- More Job opportunities: with data science shaping the way a company works, obviously R programmers demand is in the hype. Every day more job opportunities are added, giving a chance to data analysts to step up to their higher levels in their career.
- Attractive Salary: R programmers are offered good salaries in almost every company. On average, the salary of an R programmer is $71,615 per year.
- Important for data science as it becomes easy to calculate vectors, and you can run your code without using a compiler. Furthermore, it is a statistical language that lets you perform the tasks related to it.
Becoming an R programmer – Skills Required
Here are the skills you need to master to become an R programmer.
Statistics: R has more than 9100 packages that are used for performing statistical functions. The syntaxes are so easy that they let you easily import, clean and analyze data from the data sources available. A deep understanding is needed for the interpretation of the statistical output of a business.
The necessary features to be learned are hypothesis testing, inferential and descriptive statistics and probability for application in data science.
Machine learning: R is used in machine learning extensively machines prove to be best when they are programmed correctly. Sometimes, a data scientist may find it difficult to identify the trends and patterns but a machine can easily do that. You are required to develop the skills regarding the supervision of stats and help the computer learn from data so that useful information can be extracted and give useful insights.
Knowledge of Programming languages: A data scientist is expected to have an in-depth knowledge of programming languages, and definitely R is one of them. Apart from writing codes, it is needed to be an expert in using different programming environments so as to analyze the data efficiently.
Visualization Skills: Data visualization skills are highly needed to become an R programmer. Expertise in using data visualization tools like Tableau, Qlikview, Sisense, and Plotly ensures that you are capable of presenting insights to both technical and non-technical audiences.
Communication: R programmers need to be very good at communication. They are required to work with stakeholders and a lot of professionals for the solution of real-life problems. They are also required to understand the data and domain clearly on which they are working.
R programming finds its applications in machine learning, data science, and statistical computing. It is primarily a course that involves an explanation of concepts and brings the candidate into solving efficiently the real-life tasks and challenges.
If you wish to make a career in any of these applications and get good salary hikes and perks, learning R programming can prove to be the best option for you. There are online courses available to make you learn easily and land into a successful career.