Google offers Caliban as a potential solution for that problem. You can’t predict all scenarios and make sure to cover all edge cases. Often when building and developing data science projects, you may find it difficult to build a test environment that will show you your project in a real-life situation. Let’s kick this list off with a project from the tech giant, Google. №1: Google’s Caliban for Machine Learning These giant projects are great, but there are less known ones that are still used by many data scientists and will look good on your resume. When you try looking up data science projects to contribute to, you will often come across the big ones, like Pandas, Numpy and Matplotlib.
And that’s a great question, but with the exploding number of data science projects out there, finding good ones that could be the thing that lands you the job is not the easiest of tasks. That may lead you to wonder how you would find good open-source data science projects that are easy to get into and look great on your portfolio. If your portfolio has both sizes, then it means you can read, handle and debug all size software, which is a skill required for any data scientist.
Dealing with small projects is very different than dealing with large-scale ones. It should also contain projects of different sizes. However, it’s not impossible either.Ī good portfolio should include various types of projects, projects about data collecting, analytics, and visualization. Proving your skills to someone who doesn’t know you, especially in a short time frame - the average time a recruiter spends on a resume or a portfolio is 7~10 seconds - is not easy. Your portfolio needs to prove that you spent the time, effort, and resources to hone your skills as a data scientist.
One of the most crucial aspects of landing your desired role in data science is building a strong, potent, eye-catching portfolio that proves your skills and shows that you can handle large-scale projects and play nicely in a team.
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