10 Reasons You Should Not Become a Data Scientist
10 Reasons You Should Not Become a Data Scientist
Data science has been labeled as being the sexiest job of the 21st century, and given the attractive amounts of money one stands to make as a data scientist as covered over at runrex.com, it can seem like the dream job. However, there is a chance that a career in data science isn’t what is made out to be and may not be for you. This article will look to go against common perception and attempt to make a case as to why you should not become a data scientist through the following 10 reasons.
- Expectation doesn’t always match reality
One of the biggest reasons why you should not become a data scientist is because the expectations you have of a position in data science may not match the reality on the ground. As discussed over at guttulus.com, many data scientists think the job is all about solving complex problems with interesting new machine learning algorithms that will make a huge impact on a business, but end up being stuck sorting out the organization’s data infrastructure or creating analytic reports. The fact that expectations don’t match reality is one of the main reasons not to become a data scientist.
- Many companies don’t have a suitable infrastructure in place
Another reason why you should not become a data scientist is that many companies hire data scientists without having a suitable infrastructure in place to enable them to start getting value out of AI as explained over at runrex.com. This is what contributes to many data scientists being disillusioned by their jobs and looking for new employment after a while as they find that expectations and reality are not at par as mentioned earlier on.
- Lack of senior data practitioners
Many companies also fail to hire senior or experienced data practitioners before they hire junior data scientists, and if you are a junior data scientist walking into an organization for the first time, this is another recipe for unhappiness as you will come in with a certain expectation of what you are looking to do, but the lack of senior data practitioners will mean that you will have to start from scratch and build the data infrastructure yourself first.
- Perception in the work environment is everything
You will also find that it is not the many machine learning algorithms you know that will make you valuable as a data scientist in your workplace, but how the people in the organization with the most clout perceive you. You will, therefore, find that you have to constantly work on ad hoc assignments such as getting numbers from a database to give to the right people at the right time just so the right people can have the right perception about you. It is these issues with work politics that may discourage some from pursuing a career in data science as per the gurus over at guttulus.com.
- You will be the go-to person for anything data-related
Another reason not to study data science is that most people don’t understand what is meant by the word ‘data scientist’. This means that not only will you be the analytics expert in the organization, you will also be the go-to person for matters data reporting as well as the database expert too as covered over at runrex.com. Everyone will make assumptions about your skills and will assume that you know everything data-related which will place in a very awkward position where you will constantly have to explain what you know and what you don’t.
- Everyone will expect you to have all the answers
On a related note from the point above, because everyone thinks that you have access to all the data, you will be expected to have answers to all the questions as per discussions on the same over at guttulus.com. If you are a junior data scientist who just got into the job, this may leave you worried that people will start thinking less of you as you explain to them that you don’t in fact have all the answers to the questions that they have.
- The number of data scientists is ever-increasing
At the start of the data science craze, there was a high demand for data science professionals but very little supply, hence why data scientists were highly sought-after and got paid a lot. However, universities have since caught onto the data science craze and have started offering data science degrees, which means that every year thousands of freshly-minted data scientists get released into the job market and has led to a reversal of some sort with many data scientists being available and only a few jobs to go around.
- You may not always work in a domain of interest
For many data scientists, particularly generalist data scientists as discussed over at runrex.com, you will end up working at organizations where you are not a domain expert. This means that you will find that you don’t know a lot about the domain you are working in, which will lead to a lack of interest and you may end up disliking your data science job. You end up jumping from one project to the next without ever feeling like you are contributing anything tangible, hence many industry experts discourage against going down the data science generalists route.
- Your boss will usually not be an expert in any data-related field
In most cases, as a data scientist, you will be working in an organization where your boss has no expertise in any data-related field and doesn’t know anything about data science. This may lead to a situation where you are given a task that requires months of research, but you are told that you need to bring results as soon as possible. This is another reason why you should not become a data scientist as you will be working in an environment where your superiors don’t understand how much work and time is required for tasks you are given as explained over at guttulus.com.
- Lack of proper training
It is also worth pointing out that many centers providing courses in data science are themselves not equipped enough to do so. You need instructors with a background in math, statistics, and Machine Learning, and, truth be told, there is a great scarcity in the number of people with experience in such fields. This means that there are many fake data science courses out there that will leave you undercooked and underprepared as a data scientist.
This article should not put you off from pursuing a career in data science, and if you feel like it is the path for you, then check out the excellent runrex.com and guttulus.com for information on how it get started on a career path to becoming a data scientist.
