I have interacted with a lot of people wanting to pursue data science. One thing which I observed was most of them were way too concerned/afraid of going on the wrong path or facing failures. What I feel is people are afraid of committing mistakes. I constantly believe that mistakes help an individual to grow. Mistakes are a proof that you are trying. We are all humans at the end of the day and we tend to commit mistakes. Back in my first stint, my boss never saved me from the repercussions of my mistakes. I was very disappointed and sometimes I hated him too. But now, I realized the reason he never saved me was that he wanted me to learn from my mistakes and grow. So, we should always try to learn from our mistakes and not repeat them as constant repetition tends to become a behavioral characteristic
This holds true for data scientists as well. They are individuals who are responsible for deriving insights from data, forming data-driven strategies and much more. But, there are times when things just don’t work out. That’s alright! But this shouldn’t happen often as this can lead to a dead end.
We all know that the demand for data scientists is huge. Moreover, companies are substantially investing in data scientists. With this, a data scientist can’t probably afford to make mistakes. If you are planning a career in data science, I have come across 7 mistakes which data science enthusiasts should avoid while entering the field of data science.
- Don’t entirely rely only on theory
I have come across a lot of people saying, “We are learning data science from material and content available online.”
Would you ever imagine an individual to be a doctor by just gaining theoretical knowledge and no practical skills? No, right? This holds true for a data scientist as well. Data Science is an applied field and you need a lot of practice to develop the job skills. Yes, theoretical knowledge is important but it’s little need if you don’t implement it practically.
- Don’t code from scratch
To be a data scientist, something very basic that is expected from you is a strong foundation in Programming, Maths & Stats and domain expertise. One common mistake that I have witnessed among people is they start coding from scratch. With the boom of machine learning and its libraries as well as cloud-based solutions, most of the practitioners don’t prefer to code from scratch. Maybe you can do that at the very beginning for learning purposes but not for long term.
It’s a fact that algorithms are just an article of trade today. What is important these days is the application of right algorithms in the right way. So, I would suggest to pick up coding libraries like Scikit-Learn (Python) or Caret ® and start implementation.
- Don’t Run
It’s good to see so much enthusiasm within budding data scientists for building technologies, automation, robotics, etc. But, how do you build such technologies? You need to have a strong foundation in Natural Processing Language, deep learning etc. That’s how you become an expert. You cannot be a badminton champion if you don’t know how to hold a racket. Your foundation and base will help you to go a long way.
My advice is to master the building blocks and then move to mastering the algorithms and techniques of building such technologies.
- Don’t complicate your resume
I believe resumes should be very clear and specific. However, many people think that if they use fancy terms and other relative jargon, their resumes will have a larger/higher impact. If getting a job was so simple then by now we would have an unemployment rate of 0%
As an entry-level data scientist, keep your resume short and to the point. Avoid using an access of technical jargon which does not display your skills and capabilities. Describe how you ended with a solution while working on a problem, how and why did you use a particular programming language and what were the outputs. Recruiters are more interested in knowing what you can do rather than what you know.
- Don’t entirely rely on your degree for a job
Your degree might act as a proof of knowledge. But, it doesn’t prove how effective your knowledge is. Unfortunately, your degree alone cannot help in landing a job in an applied field like data science.
There is a huge difference between machine learning learned in academics and machine learning applied to industry problems. I would recommend enriching your degree by working on real datasets and developing a profile on GitHub, StackOverflow etc. Recruiters do give importance to your profile on such online platforms. Don’t miss this chance!
- Don’t narrow down your search
The word ‘data scientist’ was coined In 2008. And yet, many people aren’t aware of this term and industry. When I talk to people saying we offer full stack data science engineering program, their reaction is, “What’s that?”
Nonetheless, Data Science is growing. It’s multi-dimensional. Hence, many job positions in the field of data science are not labeled as ‘data scientist’. We have Data Engineers, Data Analysts, Machine Learning engineer etc. that have similar skills and functions to a data scientist role. Broaden your search by job responsibilities, job title, job skills etc. and get more exposure in this field.
- Don’t underestimate communication skills
Data Science is a core technical job role and hence many people assume that you aren’t required to be great in communication skills. Unfortunately, that’s not the truth. After interacting with multiple recruiters, I found that recruiters weigh a lot of importance to communication skills. This is because data scientists have to work in coordination with other departments of the company which are precisely non-technical.
You can develop your communication skills by talking to non-technical people and try explaining to them what exactly you do. Attend meetups. Meet different people from diverse background. Practice giving presentations. When you are preparing yourself for the interview, practice giving answers in a way which can have an impact on the recruiters. Of course, a positive impact!
- Don’t limit yourself
Maybe, you were cautious enough to avoid the above-mentioned mistakes, but still, you failed. No worries. I always believe that
- Try, fail
- Try, fail
- Try, fail
- Try, Success!
Never limit yourself to trying anything new. Don’t give up and emerge as a leader in the field of Data Science.