Why Data Science May Not Be For You
While I think Data Science is probably the best profession, it's not for everyone. I believe anyone can become a data scientist, but that doesn't mean they should. So, in this article, I want to go through tell-tale signs that a career as a data scientist may not be suitable for you.
I would like to note that this article is not to discourage you in any way but to be transparent about some of the realities and key aspects of the role that people may not enjoy.
Hating Math
Like it or not, math is a crucial skill for a data scientist.
When it comes to the required math level for data science roles, it's important to note that it can vary between companies and specific specialisms. However, a good starting point is typically A-level / High School math. Ideally, you should aim to be at the level of the first year or two of most university STEM (science, tech, engineering, maths) degrees.
I appreciate that this may seem quite advanced, but most top-tier data scientists I know have deep knowledge of math, typically with masters or even PhDs in subjects like math, physics, or engineering. Obviously, having a STEM background gives a big advantage, but it's by no means the be-all and end-all.
Now, you don't need to know everything in super-granular detail, but you should have an understanding of the topics and what they intuitively mean.
Sure, you can land a data science role with weak maths, but you will struggle to reach the upper echelon of practitioners because your foundations are not solid.
I appreciate that maths is not everyone's cup of tea, but if you outright despise it and don't want to invest in learning it, then I am afraid you're out of luck if you want to be a top-tier data scientist.
If you are looking for a roadmap to use, I have two full articles on the specific maths and statistics knowledge you need for an entry-level data science role.
Hating Coding
Similarly, as a data scientist, you are expected to code and be reasonably good at it.
The language of choice is Python, which has become increasingly popular over the years. Python is a relatively easy language compared to C, C++, and Rust.
Learning coding is probably slightly more accessible than learning the required maths (in my opinion), but it ultimately comes down to what works best for you and your educational background.
However, the way data science is going at the moment, we need to be more proficient coders than we were in the past few years. Nowadays, we often own some of the deployment process, so we need to be aware of software engineering best practices and our way around cloud computing systems like AWS, Google Cloud and Microsfot Azure.
As data science evolves, so must we. This means continuously upskilling and becoming better coders. If anything, we must become "full-stack data scientists" and be able to wear multiple hats within our organisations!
If you're not ready to invest time in coding and the systems around it, then perhaps data science isn't the right path for you.
If you are looking for a roadmap to use, I have two full articles on learning Python and how I learned to code.
Continual Learning
From the past two points, you might have realized that being a data scientist means constantly adapting and learning new skills yearly. We are right in the middle of all the cutting-edge AI being released, so we need to invest time in staying on top of this technology to make ourselves more valuable and efficient.
This means that we need to make a conscious effort to continually learn this information, which may require extra work and effort outside the regular 9–5. That's the brutal truth if you want to be a world-class data scientist without being left behind. Putting in more time is the simplest way to get ahead.
You must embrace a growth mindset and believe you can improve and develop skills in almost anything. Sure, it will take time, but you are confident in your abilities and will get there in the end.
If you wish to know everything about a field, data science doesn't fit that bill. I always say that "you can't complete data science;" you are, unfortunately, always on the learning hamster wheel and the wheel is getting bigger each year!
Check out my previous blog if you are interested in how I consistently learn technical topics like data science.
Imposter Syndrome
Now, piggybacking on my last point, with all these constant developments and research being released in Machine Learning and AI, it's easy to think you don't know enough, which leads to the dreaded imposter syndrome.
Wikipedia defines imposter syndrome as:
Impostor syndrome **** is a psychological occurrence. Those who have it may doubt their skills, talents, or accomplishments. They may have a persistent internalized fear of being exposed as frauds. Despite external evidence of their competence, those experiencing this phenomenon do not believe they deserve their success or luck.
In more layperson's terms, you feel like an idiot and wonder how you got to where you are, because you think you don't deserve it.
This feeling is particularly prominent in Tech as many people and companies often chase the latest buzzwords (just think "blockchain" and "AI"), which makes people working and looking to break into this field stressed and anxious.
If I am being honest, I feel like an imposter once a week, and from my conversations with other practitioners, they also experience it occasionally.
You can't escape it as a data scientist due to the size of the field and all of the active research going on with the current AI boom. It always slowly creep up on you.
Over time, you'll find that dealing with imposter syndrome becomes more manageable and it will affect you less. But being honest, it's a feeling that may never completely vanish. And that's okay.
This is something you must be prepared for when entering data science and tech in general.
Ambiguous Requirements
As a job, data science is very open in how you solve the problem. Typically, the stakeholder will come to you with a problem or an issue they are facing, and it's your job to help them solve it in some way or at least explain why it's happening using data.
There is no set process or instructions to follow; you must use your skills, expertise, experience, and associated business knowledge to devise your best solution or explanation. Now, there is obviously help from seniors and others to help guide you, but this is not always the case.
This is very much a double-edged sword. On one hand, it can be quite liberating that you can essentially "do what you want" and try out new techniques, experiments and models in your projects (an example of the growth mindset!).
On the other hand, it can be daunting to find a way to translate a specific business problem into a data science problem that you may not be fully aware of or lack that specific experience. There are many places you can go wrong or miss critical information.
This way of working may only suit some, as you must be happy and somewhat confident in often working in the unknown. I frankly love this feature of data science roles, but I appreicate it's not everyone's cup of tea.
There is also ambiguity in the job role itself, which I discuss in a previous post if you are interested in checking it out!
Rough Solutions
Real-life datasets are never as clean as those in tutorials or websites like Kaggle. Most projects require you to be a bit "messy" and "scrappy" when approaching them.
You have to find data from different parts of the business, communicate with people in various domains, and ultimately build quick and rough solutions at the beginning to demonstrate initial value and to garner quick feedback from stakeholders.
During this main project, you will probably have other small requests from stakeholders you need to answer, a pending code review in GitHub for you to look over, or preparation for a presentation you have in the coming days.
As a data scientist, it's essential to understand that your work environment is rarely clear and linear. You'll often find yourself juggling multiple tasks, adapting to a fairly chaotic way of working while striving to deliver high-quality work all at the same time.
Of course, this varies between companies and industries, but from experience and discussions, it is like this in most places. You will never have a dull day, but sometimes it will be pretty hectic!
It may be a cliche, but data science is fast-paced and dynamic!
Summary & Further Thoughts
These points are not meant to put you off becoming a data scientist; it's just to be transparent about what you can expect from a career in this field. Most of these things are why I am a data scientist in the first place. I love maths and coding and constantly learning cool things, making it a perfect career. Anyway, I hope this article sheds some light on the profession and makes you decide if this career is for you!
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