Data Science - A Skill Needed For Future

It is so much to know that every day so much of the data is being used and shared, even to notice that even data which is being shared just for information may contain insights that may help user to understand a particular observation or behavior, which may be used for research or even can be processed for analytics purpose to serve better user experience. It is even to be noticed that today's data is tomorrow's future. That being said let's understand the prospectus of so-called Data.

Take just an example that some people are buying specific clothes during a month or season. It is to be noted that it's not just about buying clothes but, the data reveals the specific type of cloth and specific range of season and what's more possible is that what is the geographic location of the common set of people that tend to buy, it helps an organization to understand about User behavior so that they can serve their consumer better by introducing variety in that stock for that season or month also with better offers or discount they can reach to more consumer so that they can get their potential sales of the clothes. 

With the above example, it has been understood how data can have more insights than just simple visible data and this brings us into the consideration for skills with analytics that can drive us into clearer and more defined data - "Data Science".

Let's understand what is Data and then we will gather into Science and eventually the meaning of Data Science. Data is raw information that will be processed in a form that has been efficient for movement processing and after processing it can deliver specific information. Whereas Science can be defined as an intellectual study that emphasizes building knowledge in form of testable explanation. This Data Science can be defined as a field of science that enables us to understand and emphasize data understandability through scientific methods, processes, algorithms to understand and achieve maximum actionable knowledge insights.

DataScience is one of the increasing IT fields which has the potential to change the future by enabling organizations or Company understand user or their customer or even any research which may require data science techniques or data science knowledge. Even some of the Universities are starting their Data Science Specialization in their curriculum.

Just like everything else DataScience has a lifecycle that mainly consists of 6 phases namely,

  • Discovery or understanding data and requirement
  • Preparing Data
  • Planning of Model
  • Model Building
  • Operation
  • Derive Results

Discovery or understanding of Result

It is very much needed before starting any result that what should be the specification, requirements should be done in prior to make the result in efficient and get the required output. The result you want to get at the end, how to get, or what demonstration needs to be abstracted from the process, in this way it will enable to visualize the model also it will lead the path to get the oriented result.

Preparing data

In this phase, the exploring, preprocess, modeling, and more takes place so that the optimized model can be applied upon requirement. The preparing of data is one of the core important that needs to be done above aging applying of model and operation.

Planning of Model

This is one of the very important steps because this is the heart or core of the requirement and expected result, it needs time and even evaluation of different models so that the later expected result can be driven with great accuracy level or percentage. It is the model wheRe the main accuracy alignS to the objective.

Operation

In this process, the whole program is carried in a way to optimize the overall requirement and performance with every optimization that can be made. The operation plays a blueprint where every module is connected and the results are derived as per requirement 

Derive Results

The final task is to derive results into an understandable format and to label each and every detail so that all the decisions can be taken upon detailed results. The format of results plays a hugely important role in understanding the complex data and getting insights from the process so that a useful data understanding and meaningful data can be classified and abstracted.

It is to be noted that the above lifecycle can be broken into minor phases depending upon the requirement and thus can be optimized as per the requirement and alter can be made.