One common piece of advice I often hear for job applicants is to have a portfolio showcasing your work. This doesn't only apply to artists or models but also to software developers and data scientists.
A portfolio of your projects acts as public evidence of your skills. This public evidence can be anything from a blog to open-source contributions to an active engagement on forums such as StackOverflow. But these types of public evidence take a long time to build.
Another type of evidence showcasing your skills is with smaller end-to-end projects.
Another type of evidence showcasing your skills is with smaller end-to-end projects. For data scientists, these can be projects such as exploratory data analysis and data visualization, classical Machine Learning on tabular data, or Deep Learning to classify images.
With the advent of large language models (LLMs) in the form of pre-trained foundation models, such as OpenAI’s GPT-3, the opportunities to build cool things with LLMs are endless. And with the emergence of developer tools, the technical barrier is getting lower.