Challenges In Data Science 



Any career will be incomplete without challenges and data science is no exception. With increasing demand and popularity, it is common that challenges will definitely be present to slow down the progress. But instead of slowing down, data science has faced these challenges in the past and continues to do so. Challenges play an equally important role when it comes to the overall development of the entire field. Some challenges that data scientists face are mentioned below.


Try Not To Be a Generalist, Be a Specialist: Every beginner in this field needs to understand the difference between a Specialist and a Generalist. There is a fine line between these two. The great data scientists don’t do everything on their own. If anyone does so, then they are generalizing things and that’s what a generalist does. Data scientists specialize in a particular area and their entire focus will be in that area only. They try to narrow down their focus to a certain area. In fact, when you’re on a road to become a data scientist, it is said that developing the basic skills comes first. Then you can go for other tools, an area of interest and platforms for deeper learning.

Hiring Newbies with Appropriate Skills: When it comes to hiring new people for the job, this is one of the most faced challenges to date. Business-related knowledge blended with the right amount of analytic skills is what companies and industries are looking for in a candidate. But most people don’t have a combination of both. Either they are good at analytics and lack a business approach or vice-versa.

With this problem still in the picture, industries sometimes struggle to create a perfect team with a balance of hardware and software infrastructure.

Trouble Having The Correct Data And An Apt Sizing: It’s no denying that the search for the correct data is still a challenge that many companies face on a daily basis. This is because of the availability of huge volume and velocity of data that will make profitable business decisions. What’s the use of that data which does not make any sense? That’s one of the reasons why data first needs to be cleaned before using it. The aim of every organization and company is to develop a robust and feasible analytical model which can only be made if the data used makes sense i.e. correct data and correct amount.  

Security Perspectives: One fact that needs to be in the list of challenges is the security of the data. Since data science is all about handling and processing the humongous amount of data, security of data is often neglected. Everybody knows the importance of security but still, it has become one of the challenges to secure the whole data. Privacy and safety of data should be considered foremost and must prevent any bit of information slipping into the wrong hands.


Resource Box

The Data science industry expects a lot of smart and diligent volunteers to be a part of this field and for that one must undertake a data science course for the complete knowledge and understanding of even the smallest aspect of data science.  


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