Data Science: Usage and Applications
The role of Data Science Applications hasn’t evolved overnight. Thanks to faster computing and cheaper storage, we can now predict outcomes in minutes, which could take several human hours to process. Below are some of the fields where we see the application of data science.
Fraud and Risk Detection
The earliest applications of data science were in Finance. Companies were fed up with bad debts and losses every year. However, they had a lot of data that use to get collected during the initial paperwork while sanctioning loans. They decided to bring in data scientists in order to rescue them from losses.
Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures, and other essential variables to analyze the probabilities of risk and default. Moreover, it also helped them to push their banking products based on customers’ purchasing power.
Healthcare
The healthcare sector, especially, receives great benefits from data science applications.
Medical Image Analysis: Procedures such as detecting tumors, artery stenosis, and organ delineation employ various different methods and frameworks like MapReduce to find optimal parameters for tasks like lung texture classification. It applies machine learning methods, support vector machines (SVM), content-based medical image indexing, and wavelet analysis for solid texture classification.
Genetics & Genomics: Data Science applications also enable an advanced level of treatment personalization through research in genetics and genomics. The goal is to understand the impact of DNA on our health and find individual biological connections between genetics, diseases, and drug response.
Data science techniques allow the integration of different kinds of data with genomic data in disease research, which provides a deeper understanding of genetic issues in reactions to particular drugs and diseases. As soon as we acquire reliable personal genome data, we will achieve a deeper understanding of human DNA. Advanced genetic risk prediction will be a major step toward more individual care.
Drug Development: The drug discovery process is highly complicated and involves many disciplines. The greatest ideas are often bounded by billions of testing, and huge financial and time expenditures. On average, it takes twelve years to make an official submission.
Data science applications and machine learning algorithms simplify and shorten this process, adding a perspective to each step from the initial screening of drug compounds to the prediction of the success rate based on biological factors. Such algorithms can forecast how the compound will act in the body using advanced mathematical modeling and simulations instead of the “lab experiments”. The idea behind computational drug discovery is to create computer model simulations as a biologically relevant network simplifying the prediction of future outcomes with high accuracy.
Virtual assistance for patients and customer support: Optimization of the clinical process builds upon the concept that in many cases it is not actually necessary for patients to visit doctors in person. A mobile application can give a more effective solution by bringing the doctor to the patient instead.
Internet Search
Now, this is probably the first thing that strikes your mind when you think of Data Science Applications. All these search engines make use of data science algorithms to deliver the best result for our searched query in a fraction of second.
Had there been no data science, Google wouldn’t have been the ‘Google’ we know today.
Targeted Advertising
If you thought Search would have been the biggest of all data science applications, here is a challenger — the entire digital marketing spectrum. Starting from the display banners on various websites to the digital billboards at the airports — almost all of them are decided by using data science algorithms.
This is the reason why digital ads have been able to get a lot higher CTR (Call-Through Rate) than traditional advertisements. They can be targeted based on a user’s past behavior.
