How to Do Data Collection for a Data Science Project

In our opinion, Data collection is not so easy for a project, especially for a data science project. Before going to collect data, you must think about the project means data science project assigned to you. There were more questions being generated rather than answers like What data ? How to collect it ? From where we start?etc. Before starting the project, you must thoroughly go through it.

The Scheme of the Project

Finally we are going to enter the reason behind this post. Before going any further we need to make some things clear. From here on, we will be using three terms for a data science project:

  1. Organization — the one for which we are doing the analytics project

  2. Customer— the ones who are at the receiving end, the users of solutions the organization makes

  3. Enabler — data scientists working on the problem, acts as a bridge between the organization and customer to convey the organization's products and services

For any analytics project, there are two perspectives or elements — organizational and customer perspective.

Organizational Perspective

This viewpoint will assist us to identify and characterize the “what”s and “how”s of the data collection associated with the organization. The main thing will be to recognize the domain of the organization, which will sift through such a significant number of "what"s. The following thing will be to comprehend the conduct of the organization — service industry or assembling? This will assist you narrow down the factors. For instance, time arrangement of worker turnover, time series of scrap volume every day, time series of machine utilization hours of the day and so on in an assembling segment will give you a thought regarding the conduct of that organization. All these behavioral data will guide you to the “what”and “how”s to a great extent and with the assistance of some research you will do just fine and dandy.

Along these lines, utilizing a hierarchical viewpoint resembles the organization directing you what it needs and will essentially help shape your objective — yet this is somewhat limited. So we move to the subsequent point of view, the second perspective.

Customer Perspective

This is actually similar to the Organizational Perspective, yet with customers. Here we manage the customer behavioral data — what is the sex? Age group?A number of past relationships with rival firms? Money related sufficiency? Love to explore different avenues regarding the most recent items?

For example, the quantity of organizations for which the employee has worked for, number of disciplinary activities, nature of units machined has a place with the customer behavioral data of an assembling industry laborer. Kindly don't befuddle the term customerwith the standard term. By  customer, we mean the person who gets and utilizes the arrangement given by the organization to beating an assembling challenge. The Customer can be a prompt or an end-client. Definition of customer varies with your objective.

Hence, customer shapes the correct arrangement or assist you with finding the "what" and "how"s. Once more, this point of view is narrow, so we need the best of both of the above mentioned.

Hybrid Perspective — Best of Both

The best approach as indicated by us, will assist you with seeing things in every one of the measurements — you get the chance to see the master plan. The decision of this point of view relies upon the trouble of gaining a hierarchical or client viewpoint. On the off chance that any of the points of view is hard to achieve, it's in every case better, to begin with, the customer perspective. As per us, customer’s can express such a significant number of words about the organization than the organization itself. According to us, customers can say so many things about the organization than the organization itself.

We hope this article will be of some help for those trying to launch their own Data Science project in the domain they desire.

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