Why You Need A Mentor To Shape Your Data Science Career

Data Science Mentor

Data scientists today struggle to devise a long term career roadmap to thrive in the data science landscape. Often aspirants plan their next move in data science by reading online articles, talking to their friends, or by looking at other professionals. While such strategies may deliver excellent results on some occasions, it can lead to a lot of failures. 

Consequently, data scientists fail to make the most out of the opportunities in the domain. To hit fewer roadblocks in their career, one should seek help from mentors. However, today, data scientists focus on sharpening their skills more than looking for a good mentor.

Undoubtedly, since the data science domain only picked up in the last few years, it wasn’t straightforward to find mentors. But the scenario has changed now. Today, one can use various platforms to engage in with the data science community and quickly get mentoring. However, data scientists only use such platforms to solve their technical problems and not career-related challenges. But in 2020, developers should start looking for mentors to make the right decisions while moving ahead in their career.

To help you understand the importance of mentors, we have laid down how you can gain an advantage in the ever-changing data science market by obtaining proper guidance.

Mentor Shows You The Right Direction

Mentors show you the right direction syncing market requirements and your skills. Data scientists easily get perplexed in the vast data science domain, failing to make decisions to progress. This is where mentors can play a significant role in your career.

Mentors can assist you in creating a plan that can help you in approaching your goal within a set time frame. The adage ‘A job well planned is half done’ fits well in this situation. One needs a perfect pan to be confident of the path for executing enthusiastically. A weak plan will create confusion, thereby hinders performance. Consequently, having a mentor will enable you to take advantage of someone who has witnessed the evolution of the landscape.

Assess Your Progress

Since the learning path is endless, it is essential to know when to decrease the pace of learning new things and indulge in mastering specific techniques. Continuously learning new technologies will make one jack of all trades and master of none. Thus, occasionally one needs to access the progress, and if required, the plan needs to be altered. Mentors’ gain experience over the years, which enable them to analyse your progress and suggest effective strategies accordingly.

Mentor Is A Motivational Force

Data scientists usually burn themself out while trying to master the skills to gain a competitive advantage. Besides, setbacks in the journey of data science can lower the self-esteem of developers. In such situations, mentors’ help is of utmost importance to get going. To find a way out of the problems and continue again without the lack of enthusiasm, one needs the support for someone who has learned to overcome such challenges. “One can also progress without a mentor, but it slackens the advancement,” said Bastin Robin, a chief data scientist at CleverInsight. “I was fortunate enough to have Anand, the CEO of Gramener, as my mentor. He has been the go-to man for guidance.”

Connects To Others Professionals

Having a mentor has other perks, too; you can take advantage of their connections and get help from other prominent professionals. Mentors can bring two or more people together for mutual aid, resulting in obtaining different perspectives and indulging in brainstorming. Expanding connection in data science is vital for learning from various experts of numerous techniques. 


In 2020, data scientists should focus on getting mentoring by prominent data scientists. One can use platforms like LinkedIn, Twitter, among others, to get a mentor by engaging in different data science-related posts. However, putting your work on these platforms will also assist you in catching the eye of other professionals, resulting in increasing the chance of getting a potential mentor. Therefore, along with data science domain knowledge, one should have a mentor to thrive in their career.

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