The importance of data is known to everyone now. If it had been 10 years ago, no one would have thought of this gigantic change in power. Data is all around us like any other gas. It is invisible but present. But there is really no value to the data as it is. This is where data science comes in and all of its algorithms that actually give it some value. Data science is, after all, about using data in creative and different ways so that they get some business value. It actually makes the data look more like a product that is ready to be sold. All data science is based on using data as input and then processing it with the help of complex data algorithms to get the necessary results. There are so many applications related to data science. Let’s see what they are.
Data Science applications around us
An important application used is the recommendation systems that are used on so many websites. Be it any eCommerce site or any video site like YouTube. Recommender systems use the input data and then generate the recommended results using the algorithms. Another example can be seen on their social networking sites. The image recognition part where we can tag people is actually based on data science. Give hints about who the person is and even say their name.
One of the biggest applications is the gaming industry. The great gaming giants are trying to take the gaming experience to the next level. They use complex data and machine learning algorithms that continually improve the user experience. Motion games, which are still particularly in their new stage, use these algorithms to learn from the user’s cues to improve the levels or the user interface.
challenges ahead
The field of data science is actually very lucrative and is actually a mine of prospects. But there are still many challenges that data scientists around the world must face. One of the biggest challenges is the fact that most companies are looking for specialists rather than generalists. They want people to really master the basics, and then choose platforms, tools, and areas they want to specialize in to gain an edge over others. Another challenge is understanding the purpose of the process they use in terms of business factors. Understanding what the customer needs and also why they need it is just as important as the algorithms that are being used. This helps to get a different perspective of the work and leads to a better understanding and in turn a better result. Another challenge that is regularly faced is explaining technical concepts to non-technical audiences. Getting the client to really understand all the intricacies of the job is a tall order. A data scientist has to communicate in such a way that both sides are comfortable to an extent that is good for everyone.