Data science – A Blend of Data Inference, Algorithm Development, and Technology


  1. What is Data Science?

  2. What is Data Analytics?

  3. What is Data Insight?

  4. Wrapping up…

What is Data Science?

People usually have a lot of questions revolving around data science, like What is data? What is Data Science? Who is a data scientist? What is Big Data? I have lots of data – now what? How are real values unlocked from your data?

Data Science is certainly something that has been on people’s minds lately. Everyone is talking about it, a lot of them are claiming to do it, and increasingly, more people are getting hired for it. But what exactly is Data Science?

In its most basic form, Data Science can be defined as obtaining insights and information or anything of value, out of data. Or as our header suggests, Data Science is a multidisciplinary blend of data inference, algorithm development, and technology which when applied to different fields can lead to incredible new insights. And people who are using it are already reaping the benefits.

But what makes something count as data? Is it a handwritten piece of paper from the year 1500? Or a book sitting on a store shelf? Are we all just data? (Okay, I kind of exaggerated the last one, but you know what I mean). So, in the context of data science, the form of data that matters is digital data. Digital data information cannot be easily interpreted by an individual, but it relies on machines for interpretation, processing, and altering. In fact, the words you are reading on your computer are an example of this. These digital letters are actually a systematic collection of 1’s and 0’s that encodes to pixels in various hues at a specific density.

Now, coming to data scientists – Who are they? What do they do? As data has become the key information that helps businesses earn benefits, it requires analysis, creative curiosity and a knack for translating tech ideas into ways to turn into profit – Enter Data Scientists. Data scientists are a new breed of analytical data experts who have the technical skills required to solve complex problems and the curiosity to explore which problems need to be solved. They’re part mathematician, part computer scientist, and part trend-spotter. And, as they straddle both business and IT worlds, they are highly sought-after and paid well. So, who wouldn’t want to be a data scientist?

data scientist

Data Scientist


Moving on to what is Big Data – It is described as a large volume of data, both structured and unstructured that inundates a business on a day-to-day basis. But the amount of data is not important; it’s what organizations do with it that matters. Big data is analyzed for insights leading to better decisions and strategic business moves. It is like a major opportunity for businesses to achieve their goals, such as enhancing customer experience, streamlining existing processes, and achieving more targeted marketing. Here are some examples of Big Data –

  1. The New York Stock Exchange generates around one terabyte of new trade data every day.

  2. Statistics show that 500+ terabytes of new data get ingested into social media sites database, every day. This data is mainly generated in terms of photo and video uploads, messages, comments, etc.

  3. A single Jet engine can generate around 10+ terabytes of data in a 30-minute flight.

But it is not enough to just collect big data. The true value comes when you use it to unveil insights that would move your business forward. So, let’s shed some light on how you can analyze big data and capitalize on the insights it can unlock.

Data and analytics build off of each other to deliver deep understanding or insights into the user base. Insights provide essential wisdom about your customers