Even with the shortage of qualified applicants, the data science job market can be quite competitive. For those trying to land their first data science job, it’s important to be more noticeable than everyone else.
One thing that has been considered the skeleton key of data science job hunting, is a strong data science portfolio. But, how do you build a data science portfolio that stands out? Well, for starters, understanding the skills you need to showcase to impress employers is what will truly make a difference.
Your data science resume and portfolio are the two things that will establish credibility and trust. This will help potential employers gauge your capacity to fulfill the role you’re applying for and the value you may bring to their company.
Creating the kind of portfolio that will open doors for you is not an easy or fast task. It takes time, patience, and lots of hours working with data. In this post, we’ll give you some tips on how to create a killer data science portfolio that will dominate over the rest.
Use Github to Gain an Edge
Having an active profile on Github and making sure you have a link to your CV is a must. Not only can you host a remote version of your project, but it also opens tremendous collaboration opportunities to improve on them.
But how active are you on Github? Github records contributions on a daily basis. And these contributions can be seen by others. And you want potential employers to see just how strong your commitment to data science is. So make sure to work on your Github profile on a regular basis.
Additionally, you should customize your Github profile page so that it better aligns with the job roles you are applying for. For example, if you’re applying for jobs in the IT or cybersecurity industry, participating and earning a badge from the GitHub Security program will definitely help you stand out.
Make Your Portfolio Relevant
Even if a potential employer is looking for an entry-level candidate, listing out projects you’ve worked on that are relevant to their business will definitely make an impression on them. If you’re applying mainly to jobs in the healthcare industry, make sure to bring focus on projects within this particular industry.
If for example you are applying to companies that follow a subscription based model, you’ll want to highlight the datasets you’ve worked with that are relevant to these types of businesses. Go the extra mile and dig in a bit into the companies you are applying for. Get a feel for the challenges they may be facing and see if you’ve addressed similar challenges in your projects. If you find that you have, make sure to bring those to the forefront for them to see.
If you don’t have anything relevant and you truly want to build a portfolio that effectively demonstrates your suitability for a role in data science, we recommend you to find relevant projects to work on. They can be one or two small projects, but this will make a huge difference when getting shortlisted for a role.
Organization is Key
You must ensure that your portfolio is clean and organized. This is where you can easily take your potential employer on a quick tour of relevant projects you’ve worked on that pertain to their specific industry or business model.
If your projects are a jumbled mess of files and folders, this will indicate a lack of attention to both detail and organization, which will significantly decrease the likelihood that someone will actually take the time to look through your portfolio.
Name your files appropriately and arrange them in an easy-to-follow manner. Clean your code and provide detailed execution instructions to make the process of viewing your portfolio an easy and pleasing experience for hiring managers. Always include a README file to help the viewer navigate easily, understand your project without having to try too hard, and be aware of any quirks in the data.
Data visualization is an important skill in and of itself. Being able to draw out a compelling story from a dataset is an incredibly valuable talent in the field of data science. By making use of robust data visualizations for all of your data projects, you will be boosting the impact of your project as a whole.
It will also enhance your storytelling, strengthen your conclusions, and demonstrate both your desire and your ability to produce superior work. Be sure to add as many comments and explanations as you deem necessary. This will help your reader follow along at every step.
Proper execution of data visualizations will showcase your creativity and knack for identifying correlating values, as well as your ability to craft a fascinating story from raw data. Remember, people love pictures, especially if they’re more interested in data-driven action than the data itself.
Participate in Competitions
Competitions help you sharpen and grow your skills. But your performance in these data science competitions can also be used as achievements that will add credibility to your work. Think of it like this: if you participate in a competition and don’t win but make it to a shortlist of hundreds of participants, this will definitely make an impression on your future employer.
Furthermore, you can choose to focus on competitions held by brands you’re interested in working for. This will help you become familiar with the brand itself and some of their challenges. At the same time, it will show your potential employer your level of interest in working for their company.
Showcasing the competitions you’ve participated in and your performance in them in your portfolio will definitely boost your chances of rising to the top.
Show Your Community Service
Are you involved with any data science communities? Do you often help answer questions from fellow data scientists or analysts? Make sure you list these out. But more importantly take this one step further by linking to answers you’ve contributed to that are relevant to the company/job role that you are applying for.
Your goal for a data science portfolio should be to stand out and be unique or one of a kind, not one of many. This will help you convince potential employers that you are uniquely qualified for a position. The final result will be a portfolio that demonstrates your data science proficiency in a way that hiring managers and potential employers can’t ignore.