Romanticizing data science as a career option

Disclaimer: This article is not aimed at belittling any profession or field as we are the staunch believers of data science ourselves. However, it is also important to peel off layers of delusion so that the harsh realities which are very much in existence, fail to trap amateurs.

Data science, a fancy term, arouses millions of expectations in the minds and hearts of those who are passionately drawn towards it to form a career in it. Artificial intelligence and data science happen to be two main fields in technology which are still young but are growing exponentially to extent that edtechs are considering incorporating them in their course structures.

You will come across a plethora of content praising data science and data scientists who are set on paths of fame and recognitions on choosing data science as their career and excelling at it. None of these praises are false or a utopic reality cooked to please the readers but the dark realities are pretty underrated and not everyone seems to be ready for the conversation.

The under-discussed truths about choosing data science as a career

1. Unexpected responsibilities

Do not be surprised if you are given to fulfill responsibilities that you had not expected initially. Take a note of the fact that data is in abundance and that companies are always anxious about data incident management. In spite installing innovative technologies to combat the problems, a human workforce is always needed for firm backing.

You may have to organize data and discard the irrelevant ones.

2. The inseparable duo of data analyses and data science

The one famous mistake that new data scientists tend to do commit is separating data analysts and data scientists whereas these two job roles are often interchangeable.

If you are a data scientist, expect the responsibility to analyze deluge of data on a daily basis. This happens only when the data are unclean and are tough to be interpreted within a short period of time by the team of data analysts.

3. Getting hands dirty

It is best to our knowledge that data is unclean. An avalanche of data is to be cleaned and processed for smooth execution. There will be no absolute model to do the job for you as problems are different and hence the processes also vary at great extents.

For this reason alone, many companies are considering installations of automated remediation that can make data analyses and organization of them in real time.

4. Big data is not that big

Being a data scientist, it is obvious for you to rely on new and effective tools to get the work quick.

But don't be staggered by the tool named Big Data because it is only a tool, total reliance on which will not you take you anywhere meaningful in the long run. Thus, it is a myth that big that can be of great help. But in order to achieve solutions to bigger problems like modeling, data processes and data scaling, big data cannot always get you through.

However, if used wisely, big data tools can be used to leverage for data analyses to ensure accuracy.

5. The two different worlds of academia and business

Enrolling yourself into a course of data science and excelling in there cannot decide your fate when you step into the business domain. The business domain cares nothing about your learning in academia.

Here the solution is to debunking the theories learnt at school and start learning what your business space teaches you. Trying to compare both will only bear you bitter fruits.

One fine example for this can be the knowledge of tools.  Schools will teach you to be perfect at that one tool that you have learnt but your business domain will expect you to be perfect at as many tools as possible. However, a handsome knowledge on few tools can get you the job but do not restrict yourself there. Keep learning.

No peach. No rainbows

No professional sphere is peaches and rainbows and resonates with your initial expectations.

The wise will have a clear mind about the reality checks to not step into the shadows and end up being demotivated.

Practice communication skills to learn from your colleagues and have an open mind to inhale every opinion that comes your way and only then you will be able to filter the relevant ones.