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


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 –

  • The New York Stock Exchange generates around one terabyte of new trade data every day.
  • 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.
  • 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 and reveal actions that can be taken to better your business. Data insights cannot be obtained without data analytics, and data analytics is useless without data. It’s a vicious circle.

Data Analytics
Data Analytics

What is Data Analytics?

Data analytics is the discovery of patterns and trends gleaned from your data. It helps to make sense of your data and uncover meaningful trends. There is huge value buried in the massive data sets, but it cannot be unlocked without the help of analytics. For instance, data from a mobile app might tell you that you sent 15,000 push messages last month. But an analytics tool could dig through this data and reveal that your app sent 3.7 messages per user, with an open rate of 20%. And that’s how your data start working for you!

In a nutshell, analytics is what provides the bird’s eye view into making sense of your data.

Data Insight
Data Insight

What is Data Insight?

Insight is the value obtained through data analytics. It is incredibly powerful and can be used to grow your business by identifying different areas of opportunity.

Continuing with our mobile app example, insights might show that sending those 3.7 push messages per user resulted in a 14% increase in purchases. Now that you know your push campaigns are effective, you can continue testing new variations and optimizing to boost purchases even further. Insights provide the “ah-ha” light bulb that sparks smart new marketing movements, powerful app features, or even streamlined UX designs.

Data insight enables companies to make smarter decisions for their business. For instance:

  • Netflix mines the data of your movie viewing patterns to understand your interest and uses it to make decisions like which Netflix original series to produce.
  • Target identifies major customer segments within its base and the unique shopping behaviors within them. This helps to guide messaging to different audiences.
  • Proctor & Gamble utilize time series models to understand future demand, which helps to plan for production levels more optimally.

Data scientists mine data insights with data exploration. When given a challenging situation, data scientists become detectives and investigate leads to understand pattern or characteristics within the data. This requires huge analytical creativity. Then, as needed, data scientists apply quantitative techniques like inferential models, segmentation analysis, synthetic control experiments, etc., to get a level deeper. The intent is to scientifically put together a forensic view of what the data is really saying.

The data-driven insight provides strategic guidance. And in this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.

Wrapping up…

Data science is an ever-evolving industry. For any company that needs to enhance its business by being more data-driven, data science is the secret. Data science projects can produce multiplicative investment returns, with guidance through data insights. If you are from a tech background and have a little something for data, then Data Science would be a great option for you. The best part? There’s a lot to do and explore in the Data Science field. It’s an umbrella term covering a number of tools and technologies – mastering any one of which will make you an asset in the ever-increasing market of Data Science.

Considering all the applications and concepts we discussed in this article, it is fair to conclude that Data Science is what the future holds for us and it is going to change the world in a big, big way.

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Data Science seems to be like an eye-opening subject to me. It is really true that in the era of over-loading information, data scientists could actually use some of that information or data to the benefits of business. I have kind of developed an interest in this field, and I will surely start reading about it more frequently.