The data science field is an exceptionally competitive market, particularly to get one of the dream jobs at one of your preferred tech organizations. The positive news is that you have it in your grasp to acquire an upper hand for such a position by preparing yourself for anything and everything. Once you get your dream job make sure to avoid several mistakes many data scientists make at the beginning of their careers.

Here are 5 data science career mistakes everyone should avoid:

Being a foot soldier instead of a thought partner

It is mostly seen that people, especially juniors sit back in the decision-making process, don't contribute their thoughts, and in the end, work on the decisions made for them. It might feel good at the beginning but later there is a possibility that stakeholders will not involve you in future meetings, and the less opportunity you will get to contribute in the future. Try to avoid this, actively participate in the discussions and maximize your impact as a data scientist.

Binding yourself into a specific area of data science

Many data scientists see themselves only as data scientists building models and don't bother to pay attention to any business aspects or data engineers who only focus on data pipelining and they don't want to know anything about the modeling that's going on in the company. Which limits not only the growth of the company but also your personal growth as a data scientist. Try to explore your other areas of data science too.

Not keeping up with the development

Being complacent about your data abilities and not investing the energy to learn new ones is a typical mistake. Doing this in the data science field is more perilous than in some different regions since data science is a field that is moderately new and is still encountering extreme changes and advancements. There are continually new calculations and devices, and surprisingly new programming languages are presented.

Over flexing your analytical muscle

It said that you should cut your dress according to your cloth. You should apply the same rule as a data scientist when you try to use Machine Learning on everything. It will be exciting at the beginning to try all the fancy models you have learned in school to solve all the real-world problems. But the real world is different from academic research, and the 80/20 rule is always at play. Take time, learn, and understand the company, stakeholders, and the world.

Someone else will build a data culture

Try not to think changing data culture is another person's work. If you need to see changes, make them. All things considered, who is better situated to assemble the data culture and teach partners about data than data researchers themselves? Assisting with developing the data culture in the organization will make your life significantly easier.