How to evade fake reviews while choosing the right institute for data science?
Data is essential for commercial and public sectors, keen on surviving the post-pandemic age. The time is for mitigation and recovery. And utmost efficiency is necessary for getting through this temporal precariousness. Data can grant the level of accuracy we need if put into experienced hands in ample amounts. The data volumes and processing capabilities required for this are also available on tap. And the dependency on the same is naturally increasing.
Data science as an avenue for success is thus becoming more and more popular. With data professionals being bestowed with more and more rewards. But to be inducted into the fraternity. A budding scientist must undergo industry incubation. And only the best institutes can assure the same.
Why are reviews important?
Reviews are first-hand accounts of people with experience being involved with an institute. They can tell us stories and reveal many essential facts about the operations and functioning of an institute. That cannot be experienced without enrollment.
But this, pure and authentic source of first-hand information has now colluded. Upstart institutes running after enrollment investments tend to use them for rendering their fake offerings seem honest and possible. These reviews are mass-produced and can be spotted with the right kind of knowledge. This article will try to bestow the same for the reader.
Essential traits of fake review
● A fake review is expected to have grammar and punctuation errors. Mainly because a bulk review producer will never take as much care as an authentic reviewer.
● The goal of a fake review is to be attractive. Therefore, they might use punctuation irregularly and even add unnecessary emojis.
● Excessive adulation in the form of stories related to irrelevant people and the product is also a sign of falsehood. These stories are created to increase the drawn attention span. Therefore, they are avoidable at all costs.
● Biases in a review can be an authentic expression. Therefore, if the reader encounters a biased review and thorough investigation must be conducted. And the reviewer’s motives must be identified.
● Competitor institutes can use an institute’s popularity to promote their offerings. And directly suggest their product as an alternative in a review. Such reviews must be ignored.
● A review with only allegations can be a strategic defamatory one. A good institute is expected to counter the same. And call the reviewer out! Those reviews are best evaluated by studying the replies.
A bastion of hope
Good data science institutes in India are not hard to find. But many are simply unable to stand up against the onslaught of fake reviews. Analytixlabs however, is extremely defensive in the case of honor and honesty. A quick search with Analytixlabs institute reviews reveals how the institute verifies all reviewers. And adds their contact details with the review for verifications. In case of a false allegation, the institute never fails to call the reviewer out and check if the grievance is genuine. And if found to be so, they try their best to find a remedy for the same.
Good institutes like ALabs exist! With the right paradigm, a student can breach the wall of lies and reach out to them. Eventually initiating a fulfilling journey in data science.
