Include the team members names as your project partners if the project was a group project and describe the tasks done by each of them, including the tools and techniques used. A data science project at work would go under the category of responsibilities and you should describe whether or not it was a group project. Mention every skill that you learn in these courses. If you have taken any online or offline data science course, include them under Education. You can start your own blog, choosing the topics that you want to talk about and think will showcase your skills well and share them to get noticed. It is always good to have a summary of your background on your LinkedIn expressing how you developed the data science skills that you possess and what your objectives are as an individual. ![]() Recruiters take notice of these accomplishments and would even be an advantage if you are not very experienced. ![]() Competing in Kaggle competitions and contributing to open-source projects are not only ways to learn and improve but also help in promoting capability in ways that courses cannot do. This specifically comes in handy because these are bigger proof of your skills. Participating in data science and machine learning competitions like the ones on Kaggle or MachineHack bear greater importance than courses certificates. We present you with a list of pointers on how you can improve your LinkedIn profile and get hired: LinkedIn is an excellent and great platform for interesting data science-related opportunities. As LinkedIn is one of the top recruiting platforms, there have been many instances where aspirants got noticed on their LinkedIn profiles and were eventually hired by companies. The data science community on LinkedIn one of the most active community on social media.
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