Does The Data Science Domain Ensure Job Security?

 By Trisha Manna at Learnbay

May 12, 2021

 

Data Science has brought a global technological revolution about which evryone is talking. With the decision of switching to the Data Science discipline, millions of professionals have already landed on lucrative career growth and guaranteed their future in recent years.

 

In the current pandemic-affected job market scenario, with a certification or a Data Science diploma, you can open up the scopes of working in this field. So, everybody is interested in moving on to a new professional stage through data science career evolution.

 

But how secure is a data science job?

 

A job market survey carried out by Analytics insight shows that around 3,037,809 new AI and ML position openings will be generated worldwide by the end of 2021.

 

This is the future scenario. Let’s have a look at the scenario of data science in the coming 5 years.

 

According to Data Flair, “The demand for data scientists is only increasing and will continue to increase in the future.”  In addition, According to the Bureau of Labour Statistics (U.S.), there will be approximately 27.9% more jobs that call for Data Science expertise by 2026.

 

So, it's clear that for the next five years, data science can be considered a secure career option. But, what's next?

 

Well, the good news is that various branches of data science like advanced machine learning, automation, deep learning, etc., are still under development. So although the fundamental job roles like data analyst, quantitative analyst, etc., might be under a threat of automation, there lies ample scope for other new openings. Hence, we can't say that data science jobs are insecure.

 

But the percentage of job security is in your hands only until you follow the below measures.

❏   Keep yourself engaged in reinforced learning

Sustainable career growth in any technical or scientific domain is not possible without reinforced learning. So without limiting yourself in monotonous data analysis, try to find out the other possible opportunities for your knowledge applications. Every time you start brushing up on your old knowledge, you will get to learn something new.

❏   Stay updated about tools and technologies.

Data science is all about tools and technology upgrades that improve the quality and performance of data handling. If you are managing your data scientist roles on the basis of Tableau, Matploitlib, or other applications, that does not mean you have no need to learn Seaborn. Maybe next year your company will switch to Seaborn or other latest software, then they will need a professional with the latest updated knowledge instead of you. So always keep an eye on trending data science tools and software and their respective updates.

 

Read technical blogs, watch 'how-to' videos, listen to 'do you know' software and gadget-related podcasts to expand your knowledge.

❏   Hold a passion for learning in-depth from hands-on experience

Maybe you have entered the data science domain as a data analyst because of your non-technical background, but it will not be appreciated if you remain rigid about your programming knowledge.

 

When you start working as a data analyst, you will find plenty of scopes to learn and apply programming knowledge for python and R. Gradually start learning the core programming.

 

Today tools are saving you, but it might be possible tomorrow your roles get automated, and only manual programmers come under the organizational demand.

 

❏   Engage yourself in online courses, certifications, and projects to upgrade your Job profile

 

Continued learning is the best way to ensure sustainable career growth. Even your present decision of a data science career switch from another domain has been an outcome of continued learning.

 

You need to maintain the same practice even after grabbing a lucrative data scientist job role. If you are now a successful data analyst or a senior data scientist, why not enroll for a certification course in Artificial Intelligence and Machine Learning Expert?

 

In case you are already in the machine learning subdomain, set a goal switching towards Automation or Deep Learning.

 

 

In case you are searching for data science, career upgradation Course in Machine Learning and AI, visit learnbay.co

 

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