Defining Data Science • What Does a Data Science Professional Do? • Data Science in Business • Use Cases for Data Science • application

1. Applications
Data science has tons of applications in real-world implementation. If I talk about applications,
all leading fields in the IT industry have their own contribution in creating some masterpiece or
the other. So, what differentiates data science from others and what is so special about them?
Do they solve our everyday problems or are they just some gimmicks, to say the least. Let's find
out!
Recommender Systems
Recommender systems have been there out for a while in the market. It uses two different types
of algorithms to suggest the content to the users - content based and collaborative based.
Content-based keeps track of the users watching habits and recommends them content similar
to their interests. Collaborative-based recognizes users with similar tastes. It then suggests
content based on their taste to each other and mostly continues to do so in the long run. For
example, Netflix has a big recommender system which suggests users web-series or shows
based on their watching habits or tastes. If a person binge-watched all five seasons of Breaking
Bad, they will be immediately greeted with the “Better Call Saul” recommendation! Such is the
power of recommender systems.
Voice And Image Recognition
Image recognition is quite common in our day-to-day lives. When we post a photo on Facebook,
we get random suggestions to tag our friends in that photo. Nowadays, smartphone cameras
are coming up with a new concept known as “AI camera”. It basically has an inbuilt machine
learning engine which identifies the name and characteristic of the object when the camera is
focused upon that object. Voice recognition is a voice command tool used in both laptops and
smartphones. Cortana, Siri, Google Now and Alexa are the popular ones in the market right
now. 70% of the times it is able to understand and translate the human voice into text.
Spam And Fraud Detection
Having more productive emails than the spam emails in their inbox is still every person's
unfulfilled fantasy! Though there are algorithms integrated with the mail to get rid of spam
emails, the application of machine learning for detection and removal of faulty emails still has a
better chance when compared to the conventional techniques. Early detection of fraud and
preventing it before it prevails has been looked after by the intrusion of data science. This way
there are fewer chances of frauds taking place meanwhile keeping the work-flow stable.
Now that we have seen and understood the reasons behind the existence of data science along
with its real-world applications, let us roll on to some important questions which you might be
curious about.