Tips to Create a Data Science Portfolio

Earlier, recruitments were based on the technical knowledge that we mentioned in the CV or resume. Today organizations demand more industry-relevant hands-on experience over theoretical knowledge. Employers look for the work the candidate has done in the industry they’re applying for. Thus having a project portfolio to showcase your skills and experience has become a mandate to land a top data-related gig. This might not be the case for every job you’re applying for, but for an industry like data science and machine learning that is still emerging, having a portfolio is essential.

Having a Data Science portfolio will help you showcase the skills that will allow you to stand out among other candidates appearing for the same job role. Without much ado, let’s understand the importance of having a data science portfolio to land a top gig as a data analyst, data scientist, or any other data-related job role.

What is a Data Science portfolio?

Like any other professional portfolio, a data science portfolio helps candidate showcase their data science skills through projects. Having a solid portfolio will highlight your ability to research questions, analyze data, gather project insights, and help recruiters understand how well you can collaborate with other team members, and the ability to communicate your findings to the users. Having a data science portfolio will help establish trust with your hiring manager and also help them assess that you have the required data science skills to do the job. An attractive data science portfolio will eliminate the need to have years of experience working in the industry.

Thus, you must carry your data science portfolio to get the dream job you thought of. If you are new to data science and do not have any idea how you can start creating a data science portfolio then the below-mentioned tips will help you get started.

Tips to Create a Data Science Portfolio

Create a GitHub Profile

With GitHub, you will be able to host the remote version of your data science project to be visible to everyone. Make sure you have an active GitHub profile so that you can add a link to it in your data science resume. Having an active GitHub profile implies that you are working on it regularly to make adequate changes and are visible to your viewers. It is a good practice to have a readme. md for your profile for customizing your homepage. A Github profile makes it easy to document any kind of data science project you’ve worked on and also helps you guide the interviewer through code and visualization efficiently.

Make the Best Use of Kaggle

If you want to practice and upgrade your data scientist skills regularly, you can pick up a few popular Kaggle datasets and work on machine learning projects. Kaggle also helps in understanding diverse machine learning techniques that you can implement while working with a given dataset. Also, you can add the link to your Kaggle account on the CV.

Create your Personal Data Science Portfolio Website

If you want to stand out among other candidates then you have to do hard work and create a portfolio blog or website where you can showcase your data science skills to impress recruiters. You can create a website using HTML coding or CMS tools like Weebly/Wix for free. You need to host that website and include the link to your data science resume. Having a website will positively impact the recruiter and make them believe that you are the right candidate for the job. You can also write a few blogs about the learnings from the data science or ML projects that you’ve worked on.

Create a LinkedIn Profile

To connect to various communities related to data science and collaborate with experienced experts in the industry, you should have a LinkedIn profile. It will help you to get better ideas and suggestions from people who have already worked on Data Science.

Well, it is not the end, you can do a lot more than this and add to your CV as per your interest in the work. The more work you add, the more are the chances of getting shortlisted for a data science interview. Also, there is a great demand in the market for a Data Scientist, and having adequate skills will help you get the role.