AI comprises of such amazing aspects titled towards problem-solving through which, a range of problems can be addressed across the technical field pretty easily. Let’s harp upon data, which is a critical part of AI and its study.
Why Should We Consider Data?
Simply, data can be defined as the collection of characters and symbols with some unexplained meaning on which operations are performed by computer. Such data is stored, analysed and transmitted in form of electrical signals while for the purpose of storage, magnetic, optical or mechanical media is put into use.
In present context, data is rightly defined as the essential fuel of present century.
Why Is Data Considered All Important?
There are too many reasons to it, such as:
- By the time electronic devices were brought into use, there was started exchange of large chunk of data. Then, as globalization takes deep to the roots in present era, we have to develop know-how for this data for business efficiency and quick decision-making and such a worth is of critical importance for other sectors as well, such as healthcare, academics, agriculture, farming, aviation etc.
Consider the fact that most of the companies make precise use of clients’ data simply to develop products or services as per latter’s preference and to keep them on their side thereby.
- Secondly, having crisp data just makes it easier for businesses to manage business operations efficiently and to initiate useful decisions in timely manner. Further, reliable data can also enable us to design visualization graphs and to draw rich insights quickly out which senior and middle level management finds extremely useful.
- Finally, as hinted above, through data, we can get to know about the customers’ interests. For instance, which service or product is largely preferred by customers and in which region, more sales are recorded or which of the areas give lesser sales to the company. Clearly, based on such insights, companies can easily initiate necessary business decisions.
Now, backstage there are skilled technical minds who perceive the logic, build programs by making best use of latest range of technologies and enable businesses to secure data and to rely upon such data and to rely upon eventual managerial decisions thereby. Such blessed data lot is called Data Scientists.
Data Science And Data Scientists:
To define data science, such is an interdisciplinary field wherein a handful of processes, scientific methods, algorithms and specifically crafted systems are integrated to gather rich insights from a vast pool of structured and unstructured data.
In case of Data scientists, such are the passion-driven professionals who remain at the centre of such game and gather inputs, run analysis on them and interpret such a complex data ocean.
However, there are certain occupational efficiencies that are at play, while data scientists live up their passion. Some of such required skills are:
- Know-how of statistics,
- Efficient manipulation of programming language like R/Python,
- Passion to extract crisp data, translate it into meaning and to load it skilfully,
- Algorithms of Machine Learning,
- Desire to chase Machine Learning at Advanced Level (i.e. Deep Learning),
- Inclination toward Data Visualization,
- Deftness to explore data and to wrangle data,
What gems does Data Science Hold for us?
Well, advantages are aplenty when one decides to study and master Data Science and opting this as a profession is a well manipulated decision. This is mainly because of the fact that most of the companies are now seen aligning it in their operations and services. Some of the benefits are outlined below:
- The skill, developed out of such intensive study, is widely sought after as companies spanning industries, have started including it, for sake of efficiency and better decision making.
- The Data Scientists draw awesome sum as salary and once one gets into it, designing programs and algos become a fun loving exercise.
- With Data Science on our side, smarter products are designed and developed while customers’ or clients’ insights and recommendations are considered too, during such development stages. As such, decisions turn out to be smarter and more sophisticated as a result.
- Data Science is versatile enough that it can be put into practice across different fields as mentioned above while one doesn’t need to study and learn data science in detail and only a slight info about any field, such as farming, banking, FMCG, insurance, finance, health, manufacturing, aviation etc, can do the trick and ultimate difference.
- There is no denying the fact that Data Science study and the skills gathered hereby, hold the key to a great career success and future belongs to this path.
Downside Of Data Science:
Now there is some downside to Data Science study and learning, as well, such as:
- It is a bit tough and complicated and to emerge a full-stack data scientist, our temples throb immensely and brain cells get over-charged as well,
- Secondly, as one has to deal with a data deluge, there is a risk of data privacy coming under threat or that private-sensitive data is slipping into wrong hands. In any of such cases, data scientists are the people coming under scanner of law or investigation.
Not surprisingly by now, data analysts carry a critical responsibility of collecting data and to arrange it with Data Science skill, simply to enable corporations to make best of the decisions in timely manner. This range from deciding upon a raw material cost to bringing transportation costs to new low to check certain issues because of which company suffers lost to arranging weekend shifts to assigning roles and responsibilities and so many other tasks, can be dealt through Data Science. As per our observation, Data Science has emerged as the supreme career to opt for and personally, it helps one to refine critical thinking department of brain and programs our brains to be more creative, logical and hence fruitful while problem solving skills are also honed. An ocean of opportunities lies ahead and there is no lying about it.
In short, with a proficient Data Engineer at the centre, he holds the key to conceptualize and create a system through which, big data is reached upto and gets under polished analysis. Further, prevailing frameworks are also redesigned to be compatible enough with novel functioning and such system units are tested too, to gauge their end performance and error detection and correction takes place too. However, roles, responsibilities and expected tasks differ from one company to another or from one industry to another.