Every field is surrounded by myths, which started by some people and spread like wildfire through the years and countries. Unfortunately, these myths are often easy to believe, and that’s why they stick around for a long time and need to be busted by the people in the field.
The internet has made it so easy for people to find information about literally anything. But, it made it challenging to spot misinformation, which allows the spread of myths about various fields. Since the tech field is one of the massive, popular, and demanding fields, it is ideal for online myths.
Although some of the tech field myths cross over to Data Science as it is a branch of tech, some unique myths are hovering over data science in particular. Before I joined the field of data science, I looked it up online and went down a rabbit hole of myths and some truths about the field, getting into it and getting a job in it.
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At first, I believed these myths, and it took me some time to discover that all that is just a lie and that data science is not as it looks from afar. So, in this article, I will walk you through the top 6 myths I heard before joining the field of data science that are completely and utterly wrong. So, if you’re considering getting into data science, this article is for you.
№1: You need a degree to get into data science
Let’s start with the biggest myth in the tech field, which is that you need a computer science degree – or equivalent – to get into any tech field, including data science. This can’t be further from the truth; yes, having a degree in computer science may make things easier, but it’s not a requirement to have a career in data science.
A better alternative for a college degree is building a solid portfolio, developing a solid experience, maybe going for a certificate of two in particular aspects of the field, and most importantly, network and building strong ties in the community. That would defiantly get you way further than having a degree.
№2: It’s all about the code
I realize that data science is a tech field, and at some point, you will need to write code to implement algorithms, collect data, clean data, and implement projects. But, data science is more than just coding; in my opinion, coding doesn’t even make %25 of the project time.
Planning, analyzing the data, creating visualizations, and sometimes even doing market research would take most of the project time and effort. So, if you think not being a good coder is a dead end, it’s not; in fact, there are some branches of data science where you don’t need to code, like creating visualizations or data analysis.
№3: There’s no creativity in data science
Perhaps the biggest myth about data science is that it is a systematic field, you follow the same steps in every project, and you will get the answer. And although there is some truth to that, there is room for a lot of creativity in data science, from finding the correct graph to obtain information in the data exploration phase to creating meaningful visualization to display your results.
Few visualization designers started as data scientists and then moved on to creating visualization as a career. And visualization is not the only way you can be creative in data science; writing code can be art; coming out with innovative communication styles are other ways you use creativity in data science.
№4: It’s hard to change fields
Another common misconception about data science is that once you get into the field and go through all the learning, you need to excel at it; you will have to stick to it or learn a whole new field from scratch. Although, at first sight, you may think that the skills needed for data science are just for data science, many of these skills are transferable.
To succeed in data science, you need to work on your visualization and communication skills, learn some business basics, and develop good teamwork skills. However, all these skills can be transferred or focused on to pursue an entirely new career if you discover that data science is not the field for you.
№5: It’s an unstable career path
If you tried Googling data science recently, then you might have come across some articles and news talking about how the tech field – in general – and data science – in particular – are unstable fields, and there is no job security when you pursue a career in those fields.
The tech field is rapidly improving; it keeps growing and evolving, but that doesn’t mean it’s unstable; it just means that it’s a field with too much space for growth and development, which is one of the main advantages of this field.
№6: Data science is hard to get into
When you’re new to data science, then you must’ve done your research, and might have been a long list of things to do to "get into" data science. But, the truth is, although data science may seem overwhelming at first, if you have a structured and timed plan, then getting into the field is not that difficult.
One more thing that makes data science a great field is how warm and welcoming the community is. If you’re having troubles with the field, I am confident that you will find a data scientist who would be happy to help you out in your journey.
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№7: You need to be super smart to be a data scientist
The last myth that we will be busting in this article is the belief of some people that to be a data scientist; you need to be a genius. I am not saying that there are not highly intelligent data scientists, but I always believed that hard work beats intelligence at any time.
If you think that data science is the field for you, and you work hard on learning the skills and tools that need to be understood, put in the time and effort, I can ensure that your hard work will pay off. Because it’s never about how smart you are, but about how much you’re willing to work.
Takeaways
For many people worldwide, getting into the tech field is appealing for so many reasons; it’s a flexible field, you can work remotely and make good money if you’re lucky. So a logical first step for anyone considering changing fields is to Google the field and read about the different job roles and options within this field.
The internet is an excellent place for information, both accurate ones, and false information. But, it is hard for those new to the field to distinguish between the actual and false and may get discouraged from pursuing the field because of the wrong information. For example, I get asked a lot about programming, quantum computing, and data science, and it shocks me how much incorrect information out there scares people off these fields.
That’s why I decided to write this article, addressing the top 6 myths that people come to me with when thinking of pursuing a career in data science. I hope this article helps people decide and have a better, more realistic look into data science and what it takes to get into the field.