Data science is a field of study that mainly deals with how we look at information. It involves gathering, analyzing, and presenting data. Businesses get a lot of data traffic. They have information about their employees, health plans, sales figures, operating costs, and many more. This is why they employ professionals who are well versed in data science.
This is important for them because if they want to find out which areas of operations they need to improve upon, the answer will be deep within that sea of data. For example, looking at claims analytics can help crack down on fraudulent or false claims. If these are found and eliminated, it will help reduce costs. They can also rest easy knowing that the right people will receive their proper benefits.
Going up to a larger scale, you encounter big data. Imagine that you are an owner of an online store and you get millions of hits on your site. Get information about each. Find out where each hit has come from, what site they have visited next, or which social media platforms they use. The variations will be practically endless.
This is where machine learning comes in. It takes some load of work off some analysts. One scenario is with online marketing. If you like to read about movies and watch trailers, you will expect to see ads about it. It will remember your browsing patterns. This is how it can make suggestions relative to your interests.
Life and Data Science
If this is all too heavy for you, it’s understandable. You can try to see it on a smaller scale. You can go down to the basics of everyday life. Data science is all around us. How so? There are some examples.
Look at your car’s dashboard. You have your instruments in there, right? Those tell you how fast you are going, how much fuel is left in your tank, if there are lights on, and others. When your car suddenly stops, it’s what you look at first. Then you only act based on what your warning indicators are telling you. If you only see the fuel gauge pointing to E, you just fill the tank up with gas. Nothing more, nothing less.
Another example, which is a bit more complex: Imagine that you are using your computer when it suddenly freezes. What do you check first? Do you go ask someone for help?
The example with the car has elements of data science. It has sensors that are linked to the warning lights in your dashboard. These are your data gatherers. The car is on the lookout for when these triggers will turn on or off. This represents your analysis. Your data is presented not just in your dashboard, but it can be with the other lights and sounds as well.
With your computer, if you ask for help immediately, it’s like hiring a data science professional. You may have done that because you are not sure how to fix it, or you don’t have the time and need to be somewhere else. Once fixed, they will let you know about it, and you can use that information in the future.
As the amount of data becomes larger, everything becomes more complex. At the end of the day, data science provided you with a solution. That is the point of its existence.