How to Build a Data Science Portfolio Project?
Why do you need portfolio projects?
If you want to prove potential employers that you really can do the data science job you are applying for, and if you don’t have prior job experience in the field, you can show your skills by building a data science portfolio. The term “portfolio” means a carefully designed package of projects, like a GitHub page or a custom built website. With these projects, you will demonstrate that you have the necessary technical skills, you are creative in devising research questions, you are able to analyze data, draw insights and collaborate with others, and you can clearly communicate your findings to audiences that might not come from a technical background.
For this purpose, we have created the Data Science/Machine Learning Portfolio Builder Class with Women In Data. I have recorded the first class yesterday and I am very excited about this class. I am enjoying preparing it, also having some fun preparing memes for it!
Overall Principles
The first class is about the data science life cycle and overall principles. The first principle is: “Expect and embrace iteration”. It is a common practice to go back and forth between data cleaning, modeling, evaluating and cleaning more or may be collecting more data, and this iterative process is what makes the results better.
The second principle is: “Enable collaboration”. When you start with your project, check out available libraries and tools. It is possible that someone else already worked on a similar problem and they shared the best practices, so you don’t need to reinvent the wheel. Also, when you are done, it is great to share your project with the world and ask for feedback, so you can improve your project and others can benefit from your insights as well.
Next, I will write about the steps of data science project life cycle and how to navigate through those steps. Keep following this series on how to build a data science portfolio in the coming posts! For those reading on, here is the link for the second post.