Data visualization is a valuable skill that can help you stand out in a competitive job market. But how do you showcase your data viz work to impress potential employers and clients? In this article, you'll learn some tips and best practices to present your data viz portfolio, projects, and process in a clear, engaging, and professional way.
The first step to present your data viz work is to choose a suitable platform to host your portfolio. You want a platform that allows you to display your data viz projects in an interactive and responsive way, as well as to provide some context and background information about them. Some popular options are Tableau Public, D3.js, GitHub Pages, or your own website. Whatever platform you choose, make sure it is easy to access, navigate, and share.
The next step is to select the data viz projects that you want to showcase in your portfolio. You don't need to include every project you've ever done, but rather focus on the ones that demonstrate your skills, creativity, and impact. Ideally, you should have a variety of projects that cover different types of data, visualizations, and domains. You should also consider the relevance and currency of your projects, and update your portfolio regularly with new or improved work.
In my experience, projects that are relevant to the job requirements should be the top priority. It will guide you on demonstrating your skills, versatility and impact in the domain that is relatable to the hiring manager.
The third step is to tell a story with your data viz work. You want to capture the attention and interest of your audience by explaining the purpose, process, and outcome of your projects. You can use text, images, videos, or audio to provide some context and narrative for your data viz work. For example, you can describe the data source, the question or problem you wanted to solve, the tools and techniques you used, the insights and recommendations you derived, and the feedback or results you received.
Instead of sharing examples for how one can tell a story, I suggest providing a structured/methodical approach - to start with a problem statement, then followed by tools and techniques used in each process from research, data sourcing, data analysis, visualisation, and deriving actionable insights.
The fourth step is to highlight your skills and competencies in data visualization. You want to show your audience that you have the technical, analytical, and design skills to create effective and appealing data viz work. You can use
tags to showcase some of the code or scripts you used, or link to your GitHub or other repositories where you store your code. You can also mention some of the data viz principles, frameworks, or best practices that you follow or apply in your work.
###### Customize your presentation
The fifth step is to customize your presentation according to your audience and goal. You want to tailor your data viz work to suit the needs and expectations of the people you want to impress or persuade. For example, if you are applying for a specific job or project, you can highlight the data viz work that is most relevant or similar to the role or domain. You can also research the company or client and use their language, style, or branding in your presentation.
###### Seek feedback
The final step is to seek feedback on your data viz work and presentation. You want to improve your data viz work and presentation by getting constructive and honest feedback from others. You can ask your peers, mentors, or experts in the field to review your portfolio and projects and give you some suggestions or tips. You can also use online platforms or communities where you can share your data viz work and get feedback from other data viz enthusiasts or professionals.
######Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
What else I'd like to add - choosing the right chart type and colour palette is key to ensure clarity, unity and inclusivity: Clarity - highlights key information and promotes information recall. Cohesiveness - ensures a smooth and unified read across multiple charts. Inclusivity - colour-blind friendliness ensures everyone in the audience fully comprehends the information conveyed.