5 Amazing Use Cases of Data Science

Data Science is a vast term and thus jotting down all the applications can be a daunting task as it is a tech omnipresent. In the present scenario, the amount of data that businesses consume is on a rise and it becomes hard to compile information across industries. Certainly, it comes as a huge advantage to the companies over their competitors to figure out the problem areas and hence work over them.

How Businesses are putting Data Science to bed? 

For businesses data science comes real handy-dandy for better marketing strategies, make informed decisions for the company. Well, there are several other areas where the applications of data are creating milestones. Let’s dive into the article for more.

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Energy Management

To be honest, we exist in a scenario where the primary concerns related to energy are optimization, distribution, and building automation. Data Analytics come to play here and concentrate on controlling and monitoring the dispatch crew or the network devices.

One such good example for the employment of data is from an energy software company, Tendril. The data scientists at Tendril opted for a hybrid approach that combined both collaborative and content filtering at their end. Being a proficient Home Energy software management company, they provide analytics and consumer-based solutions to the energy suppliers, including the energy products that the consumers are most likely to consider. This approach of there is extensively used in recommendation models that help new or existing customers to vouch for the energy products they cater to.

Also Read: How Big Data Automation changing Data Science

 
Mental Health

Its been continuously told that data science is set to bring a new set of changes in the healthcare segment. Data sets have already started showing how genes are associated with mental illness. Data about how a person grew up to the childhood impacts, can actually predict demand on healthcare services using demographic data.

For instance, Ginger.io uses data from the mobile devices of the users for viewing how users a feeling. This data comes as a major help for both doctors as well as the end users. As per Ginger, they provide access to high behavioral healthcare by leveraging their team of expert coaches, therapists, and psychiatrists with machine learning and AI technology.

They also state that their behavioral analytics engine has been built from years of research at MIT Media Lab and is intelligent enough to aggregate, encrypt and anonymise patient's data before running it through statistical analysis to create meaningful insights.

Also Read:  Key Skills a Data Scientist Needs

Credit Scoring

Being one of the most traditional applications of data science, credit scoring was introduced in 1989 with a FICO score. The score is still one widely used score for peer to peer lending though new machine learning algorithms and capture innovative factors that traditional scoring absolutely cannot.

A good instance that we wish to quote is from Ferratum Bank that used machine learning models to make better lending decisions, detect fraud more precisely and expand their customer base more efficiently than other lenders. And by employing this approach they were able to reinvent how both consumers and businesses wanted to obtain a loan.

Also Read: 6 Reasons to choose R for Data Science

Gaming

Gaming giants like EASports, Zynga, Nintendo, and others have realized the importance of machine learning and how this tech can elevate their domain. It certainly escalates the gaming experience to another level. Games are now developed using Machine Learning algorithms so that they can upgrade as the user moves to higher levels.

With present AI dominance in many games, research interest has kind of shifted to games of imperfect information. Not only these games are much harder for machines to master but also because these games mimic the challenging scenarios AI will face in the real world.

Also Read: AI Vs. Machine Learning Vs. Data Science

Image/Speech/ Character Recognition

Machine Learning uses iterative and redundant algorithms to learn data and allows systems to find information, hidden values that are not programmed. The repetitive aspect of Machine Learning is important because when these models exposed to new data, they can adapt easily. Machine Learning and Data Science can apply easily to a large set of datasets to perform face recognition and speech recognition.

One of the most common uses of Machine learning is of-course, Image and Speech Recognition. The object is classified as a digital image. For images basically, the measurements describe the outputs of each pixel in an image.

Also Read: How is Hadoop changing the future of Data Science

Similarly, speech recognition applications include voice interfaces. Interfaces like voice dialing, call routing, and domotic appliance control. The recognition can also use simple data entry, structure document production, and speech-to-text processing.

For instance, Baidu’s research and development department created a tool named as Deep Voice_ a deep neural network that is capable of producing artificial voices that is difficult to differentiate from a real human’s voice. The platform is smart to learn features in rhythm, voice, pronunciation, and vocalization to create a voice of the speaker.

So if you are geeky at heart and have a little extra for data then Data Science is your call. The bottom line is Data Science allows companies to serve their customers better, though it always ends whether or not your company is asking the right kind of questions.

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