How R relates to data science ? And what are the basics& various components of R programming ??

Data science is on a rapid growth trajectory which is finding a place for itself in all industries that exist in the world today. Healthcare and medicine are integral parts of our lives because in the face of any ailment, we rush to hospitals and surrender ourselves to their care. The traditional way of diagnosis was based on doctors, treatment was also by doctors. But technology in the healthcare industry has moved leaps and bounds in its application. Human error was taken into consideration and hence, a shift towards advancement in computers and computer-based treatments was deemed necessary to increase precision and success-rate.
Getting into the data science aspect
With the advent of data science, today, accurate diagnostic measures are obtainable. Medical imaging, genetics, **** discovery and many other fields within healthcare also depend on data science. Various imaging techniques like MRI, CT scans, X-ray etc are used to observe the inner parts of the body. With deep learning technologies of data science making its way into this technique, now, doctors are able to detect microscopic deformities in these scanned images. Image segmentation and processing also helps in enhancing and reconstructing these images.
Data science and genomics
The sequencing and analysis of genomes is called genomics. Organizations used to invest a lot in analyzing the sequence of genes before big data and data science entered the scene. It was a time-taking, expensive ordeal. Now, advanced data tools analyze and derive insights in a much shorter time and for lesser costs. Data scientists analyze genetic sequences and find correlations between parameters contained within.
Predictive analytics and healthcare
This is one of the most important domains of data analytics. The predictive model utilizes historical data to find patterns and generate accurate predictions. Various correlations and association of symptoms is done between habits and diseases and this ultimately results in meaningful predictions. This type of analytics also plays a prominent role in improving patient care, managing chronic disease and increasing efficiency of pharmaceutical logistics. A data-driven approach of preventing disease that is commonly prevalent in society is gaining momentum.
Active monitoring and prevention
Hospitals can predict the deterioration of a patient's health and provide measures of prevention using data science. This can help in early treatment to reduce risk when it comes to patient health. Data science plays a great role in IoT (Internet of Things) and these devices can help in monitoring health indicators. Wearable devices that track heart rate, temperature, and other medical parameters are widely available in the market today. There is tech that even administers the medicine, like a wrist band that pumps insulin based on the sugar levels of a patient. Such data is also collected and analyzed using analytical tools to get an over understanding about the health of the population. From calorie counting to blood pressure, doctors can monitor a patient's health from home devices itself. Use of real-time analytics when it comes to chronically ill patients is very useful in keeping track of their movements and to predict if they will face any issues on the basis of their current condition.
Resource box
Data Science is rapidly growing in the world today and is exceptionally transforming the healthcare and medical industry. Take data science courses to understand more about its applications. check this too Data science Interview Questions.