Programming languages such as R Training have made a huge impact on data science. Find out how exactly you can use AI ML for augmented analytics to pull insights from big data.
The use of Artificial Intelligence (AI) and Machine Learning (ML) has left an indelible mark on the work of data management across all major industries. Today a majority of data focused companies rely on AI ML techniques to not just analyze data quickly but also start mining data at a very advanced stage to simplify the whole data lifecycle process. There has been a very positive effect of putting AI ML at the front of data management which invariably influences the way data is analyzed and insights are delivered to various business decision making groups. In this article, we will evaluate the role of data science techniques, powered by the combination of programming languages such as Python and R, on the growing field of advanced analytics, also referred to as augmented analytics.
R training is one of the simplest programming languages deployed to simplify the various aspects of Big Data analytics. It has completely removed the part in analytics processes where programmers would spend days coding for AI ML platforms to extract insights from Big Data. Today, analysts from different departments can don the hat of a programmer with R training and assemble complex data models and query sets without a formal background in IT or coding! Yes, that’s possible because R and Python coding simply use English as a commanding platform that the software toolkit translates into advanced machine level codes. The backend programming is so simple and fast that you can instantly see the results of your program in R to detect patterns, insights, and anomalies without breaking into a sweat.
One of the most advanced augmented analytics applications is seen in the field of Social Media where R is used to perform Behavioral Analytics / Sentiment Analytics. Because R is a highly advanced statistical tool for augmented analytics, it becomes so easy to perform various statistical operations like Linear and Non-linear modeling, time series analysis, text classification, and clustering with data visualization. For social media analysis, in particular, we are seeing the practice of R training taking center stage in semi-supervised and supervised machine learning research involving Big Data, Cognitive Intelligence., NLP, CNN, and so on.
Another familiar use of R is in the field of Fintech, where many payment apps are using AI ML research to identify credit risk modeling and calculate financial losses in real time.
If you are truly interested in AI ML research, get your R certification online today!
