Top 7 strategies to grow faster as a Data Scientist by Darko Medin

Data Science is one of the top career paths today and i will share some strategies on how to be better at Data science and learn faster from my long experience as a Data Scientist.

Here are the strategies:

1.Take small steps

Data Science progress is often dependent on small steps. They are most important segments of progress in Data Science career.

2. Always go back to the basics

Even the most complex algorithms are created using the basic code blocks. If you want to understand advanced Data Science, understand the basics in detail.

3. Never stop learning

A Data Scientist should never stop learning. Why? Well, because the Data Science itself is evolving quickly and learning makes sure you are up to date with the latest knowledge in Data Science and that the capacity of your knowledge increases.

4. Don't be afraid to implement new and complex procedures or take jobs out of the comfort zone.

Many Data Scientist like to stay in the comfort zone. Instead try to go out of the comfort zone and learn to implement methods you haven't worked with before. This is one of the best ways to grow as a Data Scientist.

5. Be aware of the overall goal in every project

Instead of starting of with the project with no idea how its going to end, try to predict the overall project goal and what it takes for it to be achieved. In order to be successful in any Data Science project, try to understand exactly what it takes to define that project as successful.

6. Learn to communicate Data Science to non-Data Scientists

This is very important for any Data Scientist. Not everyone has a same perspective on Data Science as Data Scientists. Learning how to adapt to non-Data Scientists perspective will make sure the Data Science is understood by everyone in the Project and other Stakeholders.

7. Always implement what you learned in a Practical Project

Probably the most important segment from my list comes last. Data Scientists often learn, implement code, learn new things and sometimes do it too quickly. Take the time to implement what you learned on a real project. This is not just the only way finalize the learning process, but these projects a great for any Data Scientists portfolio and serve as a good evidence of Data Science skills.