Understanding Data Science

The development and highly impactful researches in the world of Computer Science and Technology has made the importance of its most fundamental and basic of concepts rise by a thousand-fold. This fundamental concept is what we have been forever referring to as data, and it is this data that only holds the key to literally everything in the world. The biggest of companies and firms of the world have built their foundation and ideologies and derive a major chunk of their income completely through data. Basically, the worth and importance of data can be understood by the mere fact that a proper store / warehouse of data is a million times more valuable than a mine of pure gold in the modern world.

Therefore, the vast expanse and intensive studies in the field of data has really opened up a lot of possibilities and gateways (in terms of a profession) wherein curating such vast quantities of data are some of the highest paying jobs a technical person can find today .


As mentioned, we are living in times where the worth of data is more than that of a mine of pure gold. And so, understanding what exactly the data contains, curating it so as to maintain its understandability and integrity throughout the period it is needed for, coming up with methodologies and tools in order to communicate with and make use of the same data, are just some of the things that the world of data science is all about.

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Data science as a single concept, however, is too broad to define in a single go for it contains a lot of aspects that have to be undertaken in a data science project-analysis, analytics, model-designing, testing, maintenance etc. are some of the smaller subcategories of tasks that have to be undertaken when we are talking about data science. In the end, the ulterior motive of data science is fairly simple, though-to understand the hidden pattern and meaning in a large pile of data that can be simultaneously used to solve some real-life problem, help businesses tackle decision-making obstacles, understand and analyze the future behavior of people as per the data trends.


A data science project comprises of a lot of things-things which are not possible to be all managed by people with a single field of expertise. Some of the professions involved in any data science project include data architects, data engineers, data analysts, data scientists etc. The work of every single one of these individuals vary widely and are heavily interdependent on each other-you may call it a symbiotic relationship with multiple entities. Talking strictly about data scientists though, the major part of their workload can be broadly categorized into 3 subsections-

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1. Organizing

Data is nothing but a random heap of unorganized junk. So, the first and foremost of the steps involves putting this data into a format that can be easily used in later stages.

2. Modeling

This stage is all about designing various models using the tools at one's disposal that will be a potential solution towards solving the problem at hand.

3. Finishing

After a final and working prototype of the model is finished, it is now time to deliver it to the client for reviewing and making any changes and refurnishes (if any is required)


Source by Shalini M