Learning from the mistakes of others, allows you to significantly shorten your learning curve and progress faster as a Data Scientist.

Mistakes shared by Thu Vu data analytics

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1. Heavily Focused on Online Courses and Certificates

You might find yourself enrolling in a bunch of online courses and not completing them. This can be very overwhelming, there is so much content and ways of explaining the same thing, which may lead to confusion. Dialing down the number of courses you take simultaneously and incorporating up-to-date Data Science books can help you reap the most benefits in your learning journey.

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2. Indecisive in What Tools to Use

Although it is important to understand what skills and programing languages are more in demand. It's also important not to allow yourself to get caught up on deciding which programming language you should start with, because it doesn't matter in the long-run. Programming skills is a highly transferable skill which means once you know how to program a certain language you can easily apply the concepts and learn another one with just a fraction of the time it took you to learn the first language.

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3. Learning in Isolation

Let's face it most of your friends aren't interested in data science. This doesn't mean you have to learn in isolation. Learning alone can discourage you from becoming a data scientist a long the way. So it's wise to seek out data science communities, meet ups and events. Involving yourself with people that share a common interest can help you in so many ways such; as making new friends, expanding your network, collaboration opportunities, learning from one another, developing new skills and abilities the list goes on...

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4. Retaining Knowledge

If you want to progress faster in acquiring knowledge and unlocking new abilities and skills, it can be wise to share what you've learned with others. Teaching or sharing your newly acquired knowledge and skills is one of the best ways to lock down what you've learned.

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5. Not working on Portfolio Projects

A common mistake aspiring data scientist make is not working on portfolio projects from the start and putting what they learned into practice. It doesn't matter how many courses you've taken, if you haven't worked on real world portfolio projects it'll be hard to measure your knowledge and skills. Learning by doing and teaching is one of the best ways to progress your knowledge and skills.

Types of portfolio projects:

• Personal projects
• Volunteer projects
• Team Projects

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