Is taking a leap from your current career path, worth the risk?
Taking risks
Leaving a job is probably one of the most difficult decisions in life that we have to make. How can someone think of replacing a stable career path for something quite uncertain?
An example is the field of Data Science. For the past decade, interest in Data Science gained so much popularity that even professionals are shifting careers to it. Statisticians, Engineers, and Data Analyst are the most common professions which are taking a risk with Data Science.
A career path with opportunities
For an established engineer, what does it take to be convinced to set away Engineering for Data Science?
Salary
Probably, the first consideration in taking up a career path is compensation. Aside from the fulfillment at work, having to be well compensated is something everyone looks forward to from a job.
In today’s industry, Data Scientist does have higher pay compared to the average salary of Engineers. The salary for Eight years of work experience as an engineer for a factory automation field is just a bit ahead from the salary of an entry-level Data Scientist.
However, when it comes to engineering consultants, there is a huge advantage, the salary is even better than that of a mid-senior data scientist.
Career Development
Since Engineering is more of the traditional industry-related jobs, experience and tenure are the basis for promotion. But, when it comes to Data Science, creativity is essential to create opportunities. Since Data Science is the road to the future, the room for innovation is quite vast and expandable. Data Science suits best the younger generations, which are more proactive and filled with creative ideas.
Scope for opportunities
Since Data Science involves tinkering with the technology of today, it is expected that it still has a lot of room for improvement. Basically, the opportunities seem to be limitless at this very moment. Though Engineering is considered to be traditional, modern learning is trying to incorporate traditional concepts in engineering to modern applications, just like in Data Science.
Soon enough, ends will meet for Engineering and Data Science, and it might create a better and improved branch of learning for future generations.
