Introduction
Data Science continues to be one of the promising and in-demand careers for skilled professionals. A data science career is rewarding and lucrative, but the way to start a career in data science is not that simple. A bachelor’s degree or a master is not required to become a data science professional. The right skill set and experience are what one needs.
This blog will help you understand the career pathways and required skills and how to start a bright career in data science.
To start a successful career in data science, you just need to have hard skill sets like analysis, machine learning, statistics, neural networks, etc. and you must be a problem solver, critical thinker, and a good storyteller to excel in data science.
What are the benefits of a Data Science Career?
The demand for data science skills has skyrocketed, this is due to the numerous amounts of data that is getting generated across the businesses and the need for effective data-driven decision making.
- High Salary:
The work experience is the largest factor in data science salaries; however, the average salary of an entry-level data science professional is higher than the median salary in the U.S.
2. Solve complex problems:
The primary job of a data science professional is to find insights by analyzing and processing a huge amount of raw data. Example: How to increase sales? To identify the hidden problems in current business operations.
Being able to extract information from data is a very powerful position to be in with data being collected in all aspects of society, ranging from marketing to health and even to sports and entertainment.”
3. Less vulnerable to Job automation:
In general, the more specialized the job (fewer workers are Doing It), the less likely it is to be automated because there is less incentive to invest the resources needed to build a machine capable of the job.
At that point, data science will be an essential skill, but one not limited to a handful of experts, and we will not need anyone trained specifically in data science. I am hopeful for this time because I have repeatedly seen the importance of domain expertise for building effective data science pipelines. If we can put the right tools in the hands of those with experience in a field, then data science can deliver on its promises of increasing efficiency and enabling objective decisions. Automation is not likely to take data science jobs, but if the right tools are developed, a data scientist may become an extraneous specialization.
Individuals who have extensive knowledge on how to convert data into actionable insights using analytical skills are the ones who have a chance to have a bright career in data science.
