Analytics and Data Science

Data Science & Insights: Overview


Data Science is a structured, rigorous, and cyclic process to leverage data for making informed decision.


Structured :

At a high level, an analytics process is well defined. Defining requirement, selecting right data analytics framework or modeling techniques, documenting the assumptions, reviewing & analyzing the outcomes, and summarizing the results & findings are the key stages involved in any of the analytics process.

Rigorous :

Analyst has to review and validate details at every stage of the analytics process to ensure that the outcome is of required quality and aligned with the business requirements.

Cyclic :

One of the interesting &challenging aspects of the analytics process is that it is engaging, evolving and cyclic. Based upon the outcome at every stage, we need to align or re-align the next course of action within that stage.

What it takes to deliver insights?


Analytics or Data Science & Insights demand you to know business domain(Defining business problem),statistics & analytics techniques,(Mapping to Right Analytics techniques),data & technology(Solving the analytical problems) and effective communications & leadership:(Deploying or getting it deployed for decision making).

Mapping of Analyst’ involvement to deliver insights


Learn/Know :

An analyst or data scientist has to be aware of analytics process, data, business problem, analytical frame work and statistical techniques and tools such as SAS &R.

Engage :

Undertake data analysis forward, validate the steps/code, and review the intermediate results and output.

Apply :

An analyst has to understand, and analyze current results, find ways to improve the results. Considering analytical process is cyclic, the analyst also requires thinking of ways to visualize and communicate the results to the business stakeholders.