Applications of Data Science
Here I am going to discuss about the various sectors that use Data Science. All these sectors extract the information from data they need to create different services and products.
Social network platforms such as Twitter, LinkedIn, facebook, Tumblr, Pinterest, instagram, YouTube and so on, collect a lot of data everyday. Which is why they have some of the most advanced data centers spread across the world. Having data centers all over the world help these companies serve their international customers better & faster without any network delay. They also help them deal effectively with the enormous amount of data.
So what do all these different sectors do with all this “big data”?
Their team of data scientists analyze all the raw data with the help of modern algorithms and data models to turn it into information. Then they use this information to build digital services, data products & information driven apps.
What are digital services, data products & information driven apps?
Food delivery apps like Uber Eats, DoorDash or Swiggy & ride-sharing services like Uber, Lyft or Ola are Data Science applications
Digital Service is one that has been entirely automated and which is controlled by the customer of the service, for example, as an “app” on a mobile phone, tablet, or PC.
Data product is a product built through the use or analysis of data in an effort to engage the consumer, organize information or solve a problem with data. For instance, an Artificial Intelligence(AI) that provides customer predictive analytics or website analytics tools such as Google Analytics.
Information Driven Apps, also called Data Driven Apps, are applications which are governed by the data that they process. These operate on a diverse set of data like spatial, sensor & transactional data, which are pulled from multiple sources. For example the Map-based applications like Food Delivery Apps or Uber/Ola Apps.
Processing Data Science in Social Network Platforms:
Let’s understand this using LinkedIn website functioning. Suppose you are a Data Scientist based in Bangalore city in India. So it’s very likely that you’d want to build connections with others related to Data Science in Bangalore. Now what social network platforms like LinkedIn does with the help of Data Science is that it looks into your profile, your posts, likes, the city you’re from, people you’re connected to & the group you belong to. Then it matches all that information with its own database to provide you with information that is most relevant to you. This information can be in the form of news updates that you might be interested in. Industry connections or professional groups that you might want to get in touch with. Or even job postings related to your field and designation. These are all examples of data services.
A day-to-day example of Data Science applications: Google Search
Let’s understand using something that we use everyday: Google’s search-engine. The Google search engine has the most unique search algorithm. It allows Machine Learning models to provide relevant search recommendations. Even as you type in the search query, you get recommendations by the help of Auto-Complete feature which is processed by Machine Learning models.
This recommendation system is perfect example of how powerful Machine Learning can be. There are several factors that influence this feature. First is Query Volume. Google’s algorithms identify unique and verifiable users that search for any particular keyword on the web. Based on that it builds a query volume. Another important factor is the Geographical location. The algorithms tag a query with the locations from where it is generated. This makes a query volume location-specific. This is one of the principal features of google search as this allows it to provide relevant search recommendations to its user based on his/her location. And finally, the Keyword match feature. Google takes up searched words/phrases and swims through web looking for similar occurrences.
Data Science in Healthcare:
How does data science help you? Today even the healthcare industry is beginning to tap-in to various applications of data science. To understand this let’s talk about wearable devices.
These devices have biometric sensors & built-in processors to gather data from your body when you’re wearing them. This processing happen in engagement dashboard which displays what your heart-rate is over a period of time, current status of your blood-pressure, how many steps you walked, how good your sleep was, how much calories you burnt for a given time, etc. It transmits this data to the big-data analytics platform via the IoT cloud/gateway. Ideally, the platform collects hundreds of thousands of data points and sends to huge databases for data storage. Then the big-data analytics platform applies data models created by data scientists and extracts the information that is relevant to you. This happens through Analytics Processing and gets sent to you in case of personal healthcare or to the authorized medical experts & doctors in the form of patient health reports.
Conclusion:
Data Science has become a crucial part of our lives now. From day-to-day google search to social network to job search to food delivery & cab service to healthcare systems, data science is present in every aspect of human existence today. As the world is becoming more and more data driven, undoubtedly, the role of Data Science is soon going to change each and every sector of work & services. In fact, the transformation has already begun!
