We have heard a lot about how the world of Big Data and Programming has evolved in the last ten-twelve years with the coming of age for C/C++, Java, Hadoop and Python. However, there is one specific programming language that has stolen the limelight in the ecosystem. That’s R.
Where is R poised to stand in 2020?
Demand for Big Data, IoT and Cloud computing is driven by the enormous amount of private and public data stashed into the current business models. The explosion in customer data has compelled data teams to look for flexible and scalable programming languages to compete in the ‘galactic’ world of hyper computing and complex networking. That’s why R is the most stable option for data analysts.
An essential tool for machine learning and data analytics teams, R Language is considered a competitive tool for the IoT, Big Data, machine learning and Cloud Computing technologies.
By 2022, R programming teams would be employed to lead 52% of the data science projects deploying machine learning and Big Data analysis with Python DevOps coming second at 35%.
When asked, 54% of the IT analysts are confident that R training combined with data science experience and IT architecture proficiency could lead them to become the most sought after professionals in the new-emerging technology verticals, including Blockchain, Crypto, AI in healthcare and machine learning for automation industries.
By far the two most popular programming languages are Python and R. However, with the recent push provided by companies like Google and Facebook, the open source community of coders and programmers, R has moved up the ladder significantly.
For instance, TensorFlow open source library for machine learning projects use R interface to provide high-level interactions to Data Science algorithms like Keras and classification models- Estimators.
In the current scenario, researchers believe there would be 25 billion connected and IoT-based devices in the world, throwing in 300 gigabytes of data everyday from around the world. To ensure businesses get the full ROI from their data aggregation and analytics effort, R programming teams are expected to grow in size and stature – in terms of salary, proficiency and data management.
By 2020, there would be a need for 200,000 t0 260,000 trained R and Python programmers who would be employed with sectors in banking, fintech, insuretech, automation and IT risk assessment. As focus shifts from software industries to Cloud, you could expect businesses saving billions of dollars in IT management. Combining the encouraging statistics from the Cloud and Big Data industry, you could expect the demand for machine learning projects truly making it to the center of all domestic and industrial applications.
Social media marketing and messaging giants like Facebook, Twitter, Snapchat and WhatsApp are already doubling their efforts to hire more people with R Training and skills. Internally, with basic 2 years training, companies are expected to become self-reliant and self-sufficient in jumping into the next stage of Industrial 4.0 revolution.
