Looking to switch careers or upskill for better roles in data science? You’ve come to the right place. Doing your due diligence on the industry can be an overwhelming task, not to mention how daunting it can be to pursue a new career in the first place. Enter, the most popular data science myths that might reign in that extra dose of fear.
Don’t worry! After designing our Master’s in Data Science curriculum to be attuned to industry demands, we know full well that these myths need to be busted once and for all. Consider this your guide to everything you need to know about a data science career and the myths you should avoid at all costs.
Let’s get into it.
1. Does a data science career require a coding background?
A data science career doesn’t always require an extensive coding background. For example, Marketing or Business analyst roles focus heavily on a marketing or business specialization rather than coding. This is because these roles require analysts to collect, clean, and interpret data.
However, at the same time, a machine learning scientist will require extensive coding knowledge as they’re constantly writing and updating the code to match their goals. The good news is that the industry offers a plethora of roles that have minimal coding requirements.
2. Do data science companies hire fresh graduates?
Many companies hire fresh graduates for data science roles. In fact, some companies prefer to hire fresh graduates because they are more teachable and willing to learn new things.
If you’re a fresh graduate who is interested in a career in data science, there are a few things you can do to increase your chances of getting hired:
- Network with people in the data science field. Attend meetups and conferences, and reach out to people on LinkedIn.
- Build a portfolio of data science projects. This will give you something to show potential employers and demonstrate your skills.
- Highlight your transferable skills. Even if you don’t have direct experience in data science, you may have transferable skills that are relevant to the role. For example, if you have experience in business development, you could highlight your communication and problem-solving skills.
3. Do you need to be a math whiz?
Absolutely not! While data science requires some mathematical and statistical knowledge, you don’t need to be a math genius to be successful in this field. Many data scientists have a strong foundation in math, but they also have strong analytical and problem-solving skills.
In addition to math, data scientists also need to be able to communicate their findings effectively. Data scientists often work with non-technical stakeholders, so they need to be able to explain complex data in a way that is easy to understand.
4. Can you YouTube your way to a data science career?
While YouTube can be a great resource for learning about data science, it’s not enough to get you a job in the field. Data science is a complex field that requires a strong foundation in various disciplines which can be difficult to hone without mentors or feedback.
Being motivated while self-learning can also be a deterrent. Finally, learning with a community gives you the opportunity to learn faster and with more peer-to-peer feedback.
5. Do you need a degree to pursue data science as a career?
While a degree in computer science can be helpful for a data science career, it’s not a fixed requirement. Many data scientists have degrees in other fields, such as mathematics, statistics, physics, economics, and even the humanities.
According to an Indeed Hiring Lab survey, the top three majors for data scientists are computer science, mathematics, and statistics. However, the survey also found that 18% of data scientists have a degree in a non-STEM field, such as business, economics, or psychology.
Transitioning to a career in data science can be challenging, but it’s not impossible.
If you’re interested in transitioning to a career in data science or advancing your current data science career to new heights, you can check out the Master’s in Applied Data Science program we have here at Vedere Institute.
