Monthly, thousands of Indians enroll in online upskilling programs with great expectations. A data science course is a guarantee of new horizons and higher positions, higher wages, and a way out of stagnation. But to most of them, that high fades away very fast. They have a good start–and somewhere between the linear regression and neural networks, they disappear.
These people are not lazy. They are ambitious, hardworking and they are hungry to grow. Then, why, have so many given up and not got themselves completed?
Data Science Course: Is it Really Progress?
Week one and week two are thrilling. You go ahead and blocked your weekends, put a Notion tracker, and you even shared your first day of learning on LinkedIn. Python basics? Sorted. Pandas? Manageable.
But after that the material becomes thick. The videos are of a longer duration. Instead of reading, it is Googling. College life starts with midterms and deadlines at the workplace begin to tug you down. No one is going to notice when you skip a week there is no professor, no roll call, and no community. You make a promise to yourself, “I will catch up next week.” You don’t.
And just in a split second, the course will become just another tab you do not open. It’s not a lack of passion or drive; you aren’t unintelligent. But because it ceased to feel worth the fight.
Why Learners Are Opting Out
It’s not just you. It is not only difficult material. It’s more than that.
One, there is no compromise in the theory. Most courses just present you with statistics and ML algorithms never even explaining why it is important. You are told to create models before you have seen what an actual dataset stored at a bank, a hospital or a ride-sharing app even looks like. It is similar to being taught how to swim on dry land.
Second, you are learning in one-on-one fashion. At a college lecture you atleast look around and see the perplexed faces looking at you too. However, online, you are only left with yourself, your doubts, and a YouTube tab. Forums are useful, at times. You are left, however, too much in the dark with unanswered questions, indecipherable feedback, and no motivation.
Third, the imposter syndrome is aggressive. You are 24 years old and are already late as far as career objectives are concerned. You are scrolling through LinkedIn and you start noticing the young guy who is 19 years old and he shares deep learning models with no big deal. The thing you are asking yourself is did you waste your time? Perhaps, you are not keen on becoming a data scientist. Maybe it was never meant to be.
Last of all, there is the expectation trap. You had entered into the assumption that you will be fit to work after 3 months. The course stated it was a beginner course. But now it is the fourth month and you cannot talk about a confusion matrix without uncertainty. And such a discrepancy between potential and reward saps your motivating power day by day until it leaves you with the original sense of failure to achieve.
How Do You Actually Sustain it?
Most people consider quitting to be a sign of weakness. It’s not. It’s design. This system was not designed to suit the real life learner-individuals who have children and also those who have the job and are in an attempt to pursue college or those who are having problem in their life and are feeling depressed. It would take playing by new rules to come out of it alive.
It must begin with ditching the thought of completing the course. That is not what it is all about. Rather, select at least one small project in which you are interested. You may want to analyze the IPL player data or may need to predict the tuition of your siblings. Theory can be helpful when you are interested in answering your questions, not abstract.
And above all, cease to measure yourself by certificates. The most effective learners do not go through all the materials. They are the ones who can convey one thing in depth, use it to make a meaningful application, and continue learning without a dashboard prompting them to.
Final Thoughts
If you have stalled on data science course, it not necessarily mean you didn’t succeed. It means that the course was not where you were at. Most of them are made to cater to ideal learners- individuals who have all the extra time, information and resources at their disposal. Most of India is not like that.
The positive thing is, you are not required to begin all over again. It is not that you are behind but you have not been instructed how to remain in the game.
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