Hire deeply-vetted R developers

R is a programming language and a software package used for statistical analysis and research. It comprises many techniques, such as linear regression, time series analysis, statistical inference, to name a few.

R is not only a software package but a full-fledged programming language, and you can create objects, functions, and packages. Though most R libraries are written in the R language itself, C, C++, and Fortran libraries are used for the heavier and complex machine learning processes.

Though the R developers are very high in demand and can be a great asset to your organization, hiring an R developer can be very tedious and time-consuming. Hiring an R developer itself demands that a person have a fair bit of experience in the language. If you are a non-technical manager, this could be a great source of information to help you hire the best R developer for your business requirements.

A data scientist and analyst must have a working knowledge of the languages such as C++, SAS, Python, Hadoop, etc. But not only knowing the languages is essential, being comfortable with the environment of language is highly crucial. It helps the scientist be versatile and solve many problems that require different approaches at any given moment. So, a data scientist must know the language inside out to be competent to analyze data and derive meaning from the heaps of arbitrary data present in random data sets.

As an R developer, it is crucial to have expertise in statistical calculations. Data analysis and statistics are closely related concepts. R has more than 9100 packages at its disposal to manipulate data but to understand the output of the language, a developer needs to have a strong understanding of the statistical frameworks and calculations.

It becomes essential for an R developer to have data visualization skills. A data visualization tool such as Tableau, Sisense, Qlikview, Ploty, etc., will help the non-technical audience to understand complex trends and analysis through charts, graphs, histograms, etc. A data scientist can cater to a broader range of audiences in an organization through data visualization tools.

An experienced R developer must be familiar with Artificial Intelligence and Machine Learning concepts to develop efficient data models and software applications. They should have a deep understanding of ML algorithms and libraries used to build intelligent applications and train models to process raw information into meaningful data.

These skills are essential for a data scientist and an R developer. It becomes incredibly crucial for small incremental optimization in the algorithms. Even though there are thousands of packages available for the R developers, it can be beneficial for the organization if they can make their in-house implementations and algorithms from scratch tailored to the specific needs.

There is no such thing as a perfect data set. In reality, there are imperfections in data sets every time. Data wrangling helps to manipulate data to remove all the imperfections in the data sets. Mastering data wrangling concepts is essential because it is the fundamental step before the actual implementation of R language to analyze the data.

Some scenarios in the organization demand to develop a whole new process altogether or tweak the earlier methods to achieve better ROI. Having a strong data intuition is helpful for R developers to think out of the box and devise innovative solutions.

It is crucial to communicate the complex data structures and trends through the visual elements to the non-technical stakeholders. Strong communication and interpersonal skills are required for R developers to grow rapidly in their careers.