5 min
As data science domain continues to make an impact on almost every sphere in business, more and more people are opting it as their career option. Be it college graduates or working professional, this “sexiest job of the 21st century” is attracting a massive workforce. However, not everyone who joins this tribe succeeds — either they are not as knowledgeable as they should be, or they feel so overwhelmed that they step on some of the worst “career landmines”.
In this article, we take a look at the career landmines that every data science should avoid:
Data Science Is Vast, Take A Deep Dive
Limiting oneself — that is one of the most brutal career landmines that professionals step on, and this is most common with people who are just starting to understand. Data science is not limited to extracting insights, it has a lot of subdomains. When you are working in this domain, make sure you do not limit yourself to one field of work. If you want to excel in your career, you have to have a complete know-how of the subject — you have to know other subdomains as well. This career landmine of sticking to one part of the sector can anchor your career.Someone Who Is Replaceable
The imperativeness of data science is so much that companies across the world are looking to hire the best talent. There are instances when companies have sacked employees and inboarded someone who is better than the previous professionals. A data scientist should not only stick to extracting value out of data, s/he has to be X% scientist, Y% software engineer, and Z% hacker, and also a good storyteller to be a complete data science professional. So, if you want to excel your career in the data science domain, you have to become someone who is irreplaceable. Keep polishing your skills, keep expanding your knowledge — whether it's through books or a course or online resource or anything, just make sure that you know what is happening in the industry and you are capable enough to work on that. And being incredibly good at what you do and being irreplaceable gives you a lot of privileges compared to other professionals.Losing Your Grip On The Basics
The data science field is so fancy that the newcomers most of the time try to learn the advanced and sophisticated things. While doing so, they forget that they have not given enough time to learn the complete basics. This shouldn’t be the case. When you are a data scientist, you are trusted by your employer that you would solve some of the most complex business problems and you can’t do that if your basics are not clear. For example, jumping right into all the coding and machine learning, assess yourself, whether your basics are strong enough. For example, mathematics is the bedrock and if you are working on machine learning or any other techniques of data science, you have to have strong and deep mathematical skills and knowledge. It’s okay if you are not summa cum laude from any top or fancy-sounding engineering institute or even a college dropout— you can always make the best use of the resources available on the internet, even if you are starting from scratch.Taking Up A Position That Doesn’t Fit You Well
The paycheck is definitely intriguing in data science. And as the companies have started to realise the importance of data science in business, they are spending a massive amount of money on their data science department — be it about getting the best-in-class data science tools or about hiring the best talent by paying a handsome paycheck. However, this paycheck might turn things around for you if you take up a wrong job role based on the paycheck. Therefore, do not accept a job role where things go over your head right now. Sometimes, if not the paycheck, then the designation might be tempting, but accept it only if you are able to take the responsibilities or if are well-read enough for that job role. There is a concept about “stepping outside your comfort zone”, this doesn’t work all the time. So, make sure, if you are not ready for such a role, do not switch or accept. Stick to what you are doing and expand your knowledge and skills.Joining The Wrong Industry
The role of a data scientist varies depending on the industry. Not every industry needs a data scientist who would program, work on machine learning, deep learning etc. There are companies from different industries that seek data science professionals just to help them extract insights from cluttered data. So, if you are someone who wants to go beyond just data extraction or generic data science job, then check the industry as well job description properly. If you are a newcomer, then there is no harm in taking up that job role; however, make sure you don’t lose your grip on other skills while working there for a long time. Sometimes, many data scientists don’t get that much-needed exposure just because they work in an industry that doesn’t have much to offer.Lack Of A Portfolio
Then demand for a top data scientist is on the rise, and to work for companies that are paying the most anticipated handsome paycheck along with significant exposure, you have to have a strong portfolio — a track record of the work you have done so far. If you want to know how to build a strong portfolio, you can take a look at the following articles:- 5 Ways To Build A Winning Data Science Portfolio That’ll Get You Hired
- 7 Tips To Make Your Data Science GitHub Portfolio Perfect
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