In this blog we are going to check the career path of an R programmer. Here we are going to find answers to some questions that came to your mind while starting an R programming tutorial.
Let us check every fact in the path of an R programmer.
R is an interpreted programming language which is designed and developed by Ross Ihaka and Robert Gentlemen in 1993. It is an open-source programing language, which is now developed by the R Development core team. R programming has a software environment, which can be used for statistical information, graphical representations, data modeling, etc.
In other words, we can say that R is a different implementation of the S programming language. Before the R getting popular, the top companies used SAS and SPSS tools for statistical analysis and graphical representations. Now the entry of R programming is a game-changer as it is open source and has high versatility. Now R programming is an innovative trending technology in data science.
In the present scenario, there are a huge number of big companies like Google, Twitter, Facebook(Meta), Airbnb, Novartis, John Deere, HSBC, and many others using R programming and many more companies are switching to R programming for forecasting, data analysis, graphical representations, etc. It means the number of job positions and the requirement for skilled and experienced R programmers is increasing every day.
According to the studies, there are more than 1 million job openings for R programmers and there are millions of job offers in the data science field which use R programming at its core.
Inside a company or in data science, the R programmer’s role will be like
Data Scientist’s job is one of the most demanding jobs in the present situation and most of the job titles are vacant because of the unavailability of experienced and skilled professionals.
Data scientists are the professionals who extract the data from different sources, make the data clean, and transform it into a readable structured format, then Analyze the data and find the patterns to get forecast and predictions and customer behavior from that data.
R programming has a wide set of tools and libraries and machine learning tools for doing this task easily and can make a good data scientist.
Data analysts are the professional who analyzes the data, mine data, and extracts the patterns and details from that data which helps to make data-driven decisions for the business forecast.
Data analyst professionals work in cooperation with the management team and must have 1 to 3 years of experience. They must have high skills in handling complex data sets and have good knowledge of technical and analytical features. They also need high knowledge of R statistical libraries, various R tools, and some machine learning algorithms.
A business analyst is a professional who can able to make intelligent solutions, which are highly technical that can able to solve the problems in business. They can able to calculate forecasts from the data from different sources and communicate with complex data to get the solution.
For being a business analyst, you must have strong business skills and knowledge about the business with a minimum 3-year of experience. R language has a huge set of intelligent tools and packages, which help the business analyst to make the job easy.
Business Intelligence experts are professionals who analyze the data and make business strategies using that data. They are also involved in critical decision-making with respect to the data analyzed. For a business intelligence expert, it needs minimum of 4 years of experience.
A visual data can be highly effective and worthy of analyzing and forecasting. R programming provides many libraries for data visualization and thereby it provides a good job position for R programmers.
R programming had different packages like ggplot2, plotly, etc for providing the visual representation of data.
We already discussed R programming had immense potential for doing statistical analysis and forecasting. A quantitative analyst job will be in Finance, banking, telecom, etc for statistical analysis of data that helps in quantitative analysis. R professional who likes finance or banking field can apply to these positions.
Many business domains are using a huge amount of data and their processing, which needs a Data Architect. The role of a Data architect is to maintain the data sources and must be compatible with the latest technologies like a spark, etc. They must have knowledge in data warehousing, BI tools, Data modeling, Database designing, etc.
This professional must have knowledge in R programming for analyzing the statistical data and examining the spatial and spatiotemp datasets.
With High knowledge in R programming, A professional can apply for a database administrator position who designs and manage the database. They must provide data backup and recovery mechanisms.
As R programming is an emerging language more and more positions and job openings are being created. So study R and achieve high future GOOD LUCK!