9 Mistakes to Avoid When Starting Your Career in Data Science

Studying data science can be interesting, and the majority of students adore their university and college days. However, after finishing the degree and after all data science course fees have been paid, it’s time to start a career.

Beginning such an important chapter in life is stressful for anyone, not just data science graduates. That’s typically the case because the secure studying days are over, and it’s time to go into the real world. While working as a data scientist can be excellent, there still are some mistakes almost all beginners make.

So, if you’re looking to start your data science career, check out these nine most common mistakes and ways to avoid them.

Mistakes to Avoid When Starting Your Career in Data Science

1. Focusing on Theory

The first mistake when starting a data science career is focusing too much on theory. Don’t get me wrong, theory is important, and finding the perfect balance between theory and practical work is the key to success.

However, most beginners spend too much time on the theory, which isn’t as crucial in the business as it is in academia. Instead of theory, work more on practical work, as data science is an applied field and requires practice for better end-results.

2. Relying on Your Degree

Getting a degree is an excellent step towards starting your career in data science. However, numerous people stop their education and improvement here, thinking their degree is more than enough to launch their careers.

Instead of stopping after getting a degree, look for some certificates, projects, and internships to improve and practice your applied skills. After that, you’ll have a much stronger CV and combined education, making you a perfect data scientist.
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3. Diving Into Advanced Topics

Most starting data scientists are guilty of jumping straight into complicated topics when they get hired. It can only lead to you having many overtime hours trying to figure things out in a new company and being overworked just several weeks in.

Instead, start with the basics until you get familiar with the company and get the hang of everything. Then, you can slowly build your way up.

4. Taking Too Much Work

Besides focusing on complicated topics, another mistake starting data scientists make is taking too much work at once. The typical reason for this is young data scientists’ desire to show and prove their skills, knowledge, and capability in a new environment.

It can quickly turn into you spending your whole days juggling between several projects and failing to meet the deadlines.

Our advice is to start small. You can take an additional project, but don’t forget you need to have some free time too!

5. Neglecting Exploration

When starting a career, most data scientists focus on what the company needs. Although this should be the case, don’t forget you need to spend some time exploring and researching in your free time. It can bring a fresh outlook on a problem you’re working on and you’ll benefit from it in the future too. So, remember to stay up-to-date by occasional exploring.

6. Studying Inconsistently

Inconsistent studying isn’t only challenging for beginner data scientists but senior ones as well. Keeping your practical skills sharp is a must if you want to be successful in what you do, so constant studying is the only solution.

For example, don’t study for the entire month only to have a three-month break. By this time, you’ll have to start from scratch due to forgetting the majority of things learned.
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7. Neglecting Communication Skills

Communication skills are a must in any business. However, as most people have some courses during their education, this is where data scientists lack. So, don’t forget to brush up on your communication skills before starting a career, as it’ll help you achieve better results during job interviews, meetings, and presentations.

8. Avoiding Competitions and Discussions

Most people tend to shy away from things out of their comfort zone. This typically includes competitions, discussions, and debates for data scientists. But, if you want to learn something and work on all your skills at once, try this out!

Even if you don’t get the results you hoped for, you’ll leave the event with one big experience as a plus.

9. Neglecting Networking

Communication skills also come in handy when building relationships and networks in the data science field. It’s important to maintain relevance, so don’t neglect networking with your employers, colleagues, or other data scientists. You can gain a work partner but a friend too.
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Conclusion

Finally, data science is much more than finishing a degree and working on projects. Start things slow at first to achieve better results in the long run. Don’t overwork yourself in the hopes of proving you’re a good worker. Instead, spend some time working on other useful skills. In some situations, they can be even more important than what you’ve been studying for.