Data science process: a comprehensive description

Data is power in 2022. The adept utilisation of data determines the chances of success in the commercial and public sectors. In these very troubled times, a data scientist is thus a beacon of hope for ventures and organisations of all stature. Thus the public and commercial sectors alike are experiencing a host of data-dependent ventures. And the ones not utilising data enough are seen to go extinct under the pressure of pandemics and recession. The data science process is mainly concerned with obtaining data in an ethical manner and utilising the same to make effective predictions. And this article will dive into the depths of this incredible process of data analysis. 

The pre-requisites 

A secure data source

We, during our day-to-day activities, generate a lot of data just by utilising our devices and gadgets that are connected to the internet. This data just a couple of years ago was out of our reach. Due to the limitations in storage and processing power. But now we can utilise and store all the data we need. And humanity is eager to exploit this newfound power. The data we need can be obtained from vendors concerned with the trade of data. In addition to that, data can also be obtained directly from users through surveys and rating campaigns. In both cases, the approach is ethical and does not violate consumer privacy. 


The processing capability and storage space needed for the kind of data we are discussing is gargantuan, to say the least. Thus an organisation aiming to use maximum data for increasing its operational capabilities and secure a stable future must possess the necessary computational prowess. Handling and making sense of large chunks of data is not always humanly possible and requires the support of automation tools powered by AI, machine learning and deep learning. And training all of these amenities requires a lot of relevant data. The need for dedicated human resources also helped in the inception of roles such as data analysts and business analysts concerned with the data science process and prescribing the safest courses of action. 

The data science process 

Data formatting 

The data obtained from various sources is not always workable. Neither by humans nor automated analysis tools. Thus the data needs to be formatted and filtered based on interrelationships in-between domains and values. Irrelevant data and noises are cleared and forgery is often conducted for the preservation of consistency. 

Data analysis 

The formatted and cleaned data is then analysed by a data scientist. The goal is to notice patterns and similarities. Automation tools recognise certain formats of data and can be deployed only in scenarios where this formatted data is available. As the process is humanly impossible to undertake and errors can accumulate into disasters with detrimental consequences. The tools recognise patterns and correlations between different kinds of data. And conclusions are formed based on the analysis. 


Prediction of future circumstances includes careful consideration of a plethora of factors that can affect a business or commercial entity. A data scientist takes into consideration all the necessary aspects of both internal and external origins in order to understand the future implications and sustainability of steps taken in the present. These predictions are to get more and more accurate with time and increase the volume of data used in the process. The predictions are the most essential aspect of an analysis operation. As the accuracy of predictions determines the security a set of decisions made in the present can ensure. Thus the data scientist at the helm must make sure of the reliability and validity of these predictions. 

Prescription and representation 

The prescriptions are made based on the predictions. The data scientist is responsible for the communication of the same. The representation of data analysis must be lucid and easy to understand for all the interested and relevant parties. Even if they fail to understand the implications of data by themselves, data scientists must make sure that they do. The visual representations and necessary modifications needed for divisional and team-based communications must be taken care of as well. The goal of making representations this lucid is to ensure the flow of information to individual levels. The data science process is also concerned with this flow of data. The very effectiveness of analysis is dependent upon this communication and the approach of communication. So that all the involved parties manage to understand the bigger picture and play the part they should be playing. 

About the author

Debbie Echols

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