“Sexiest Job in the world”. This is the title that has been given to data science. Without any doubt, it is the most amazing job that pays really well. It is a very lucrative job monetarily because entry-level data scientists make on average $84K a year and depending upon experience and negotiation skills it goes up to 400K a year. Yeah, that is right.
But we are not here to discuss this, Are We?
We are here to answer the question: Why you should not become a data scientist?
The answer is divided into 3 Parts
Technical Skills
Soft Skills
Human Nature
Reasons not to Become a Data Scientist
Everyone has his own abilities and standards to handle problems and only a few people choose to master skills by leveraging the problems that are bestowed upon them.
Technical skills
Math and Statistics:
The field of data science is heavily focused on maths and statistics, so you’re unlikely to love the work if you’re not a fan of these disciplines. By the way, you do not have to be a math nerd. You just have to handle math problems happily because you need statistics and probability for Machine Learning and Deep Learning Algorithms.
Coding:
Data scientists need to be able to code in order to clean and analyze data, build machine-learning models, and create visualizations. If you’re not comfortable programming, this could be a major barrier to entry. Python or Julia are the easiest possible languages for data science but Python is recommended as it has much better support from the Python community.
Constant learning:
Change is inevitable and Data science is a rapidly evolving field, so it’s important to learn new tools and techniques on a regular basis for example, Large Language Models are the talk of the day and businesses have to conduct experiments and research to figure out what they can do with it. So, If you’re not comfortable with constant learning, data science might not be the right career for you.
Soft skills
Communication:
Data scientists need to be able to communicate their findings to both technical and non-technical audiences. If you’re not comfortable speaking and writing clearly, this could be a challenge. In every field, Communication is one of the key indicators of people’s success. We are perceived as we communicate with our appearance and words.
Team Work:
Data scientists often work on cross-functional teams with engineers, product managers, and other data scientists. Because collaboration with your stakeholders is mandatory for the project’s success. If you prefer to work independently, data science might not be the best fit for you.
Wizard:
Data science problems are often complex and ambiguous, and there is no one right answer. You might find yourself convicted of knowing nothing and then figuring out most of the things yourself or with a bit of help. Therefore, If you’re looking for a job with clear-cut answers, data science might not be the right choice.
Naturalistic Preferences
Work-life Balancer:
Data scientists often have to work long hours, especially when they’re working on tight deadlines. Building machine learning algorithms is gonna take some time and the only thing that data scientists can do to reduce that time is to work tirelessly and meet the deadlines. So, If you’re looking for a job with a good work-life balance, data science is not your best option.
Not Coolheaded:
Data science can be a stressful job, especially when you’re working on high-stakes projects. These projects could be really sensitive either ethically or financially with congested deadlines and with uneasiness of not knowing the outcome at all. However, if you’re not good at handling stress, data science might not be the right career for you.
Competitive:
Data science is a very competitive field, and it can be difficult to find a job, especially if you’re just starting out. I mean, it pays really well, it is the sexiest job of the 21st century, and it will be competitive. you need to be comfortable with competition, otherwise, data science is gonna throw you down.
Final thoughts
Data science is an amazing career for people who are enthusiastic about math, statistics, coding, and solving problems. However, it’s important to be realistic about the challenges and demands of the job before deciding whether or not to pursue a career in data science.
Now if you think, you have the guts to endure the stressful and ambiguous world of data science. Congratulations, you are officially signed up for an exciting career and the only thing you need is the perseverance and Course Outline for the Data Science Mastery.
Now Go, Outshine the SUN!
Top comments (2)
I was into data science back in Canada and after my business failed I ended pivoting into technical writing/marketing SEO. Less of a headache. :)
As long as our work fascinates us, we should enjoy it. Yes, Sticking up is good most of the time. But, we should do what works best for us and what we love. Technical Writing is amazing as you have mentioned. I enjoy it as well.