As a data analyst, you have probably worked on various projects that showcase your skills and knowledge. But how do you share your data analytics portfolio with potential employers, clients, or collaborators? In this article, we will discuss some tips and best practices for creating and presenting your data analytics portfolio online.
The first step is to choose a platform that suits your needs and preferences. There are many options available, such as personal websites, blogs, GitHub, LinkedIn, Medium, or online portfolio services. Each platform has its own advantages and disadvantages, so you should consider factors such as ease of use, customization, audience, feedback, and cost. Ideally, you want a platform that allows you to display your projects in an organized, attractive, and interactive way, and that also lets you showcase your personality and professional brand.
Besides branding and accessibility, cybersecurity is really important. Blockchain innovators will benefit from the compatibility of dataset sharing and collaboration underpinned by blockchain vis a vis cybersecurity.
In my experience creating a Wix site helped me to be tailor my portfolio to how I want people to navigate it. But I'll also post my projects in LinkedIn articles (as well as on my site) as I think it allows people to easily interact. But Wix takes time! So you can use other sites e.g. Maven Analytics, carrd.co to create faster but still looks good (Luke Barousse has a YT walkthrough). Other advice I've heard is to use GitHub Pages, as while it involves coding (there are walkthroughs on YouTube e.g. Alex the Analyst). It shows you're tech savvy. Others say use GitHub itself both to show code and as portfolio site (using Readme section for write ups). You can get good at it and can look great, but I find sites more visually impactful.
The next step is to select your projects that demonstrate your data analytics skills and experience. You don't need to include every project you have ever done, but rather focus on the ones that are relevant, impressive, and diverse. You should aim for quality over quantity, and highlight your strengths and areas of expertise. For each project, you should provide some context, such as the problem statement, the data source, the tools and methods used, the results and insights, and the impact and value. You should also include some visual elements, such as charts, graphs, dashboards, or screenshots, to make your portfolio more engaging and informative.
Unlike software projects, data analytics projects reveal many details about the organization from where you sourced the data. It's very important to keep the dataset confidential and anonymize things that reveal private information. Ideally, you should stick to public datasets to showcase your skills or modify the dataset, which is crucial to the organization.
The third step is to write your stories that explain your data analytics process and outcomes. Your stories should not be just a list of facts and figures, but rather a narrative that showcases your creativity, curiosity, and problem-solving skills. You should tell your stories in a clear, concise, and compelling way, using simple and accessible language. You should also highlight your challenges and learnings, as well as your recommendations and actions. Your stories should answer questions such as: Why did you choose this project? How did you approach the data analysis? What did you discover and what did it mean? How did you communicate and apply your findings?
This is the most crucial part of your project, and it's imperative to add what type of target audience the project is meant for, e.g., a high-level dashboard is suitable for top management. Still, a more detailed-level dashboard is required at the functional level.
The fourth step is to showcase your code that supports your data analytics projects. Your code should be well-written, documented, and formatted, following the best practices and standards of your chosen programming language. You should also use appropriate comments, variable names, and functions to make your code easy to read and understand. You should use
tags to display your code blocks in your portfolio, and provide links to your GitHub repositories or other sources where your code can be accessed and downloaded. You should also explain your code snippets and how they relate to your data analysis and results.
###### Update your portfolio
The fifth step is to update your portfolio regularly with new and improved projects. You should keep your portfolio fresh and relevant, reflecting your current skills and interests. You should also review your portfolio periodically and remove or update any outdated or irrelevant projects. You should also seek feedback from others, such as peers, mentors, or experts, and incorporate their suggestions and critiques to enhance your portfolio. You should also track your portfolio performance and analytics, such as views, likes, shares, or comments, and use them to measure your impact and reach.
###### Promote your portfolio
The sixth and final step is to promote your portfolio to your target audience and network. You should share your portfolio on social media, online forums, or other platforms where your potential employers, clients, or collaborators can find you. You should also include your portfolio link in your resume, cover letter, email signature, or business card. You should also participate in online communities, events, or challenges related to data analytics, and showcase your portfolio as a way to demonstrate your skills and knowledge. You should also be prepared to talk about your portfolio in interviews, pitches, or presentations, and highlight your achievements and value proposition.
######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?
As Product Lead working at a B2B startup in Real-time analytics and Anomaly detection, here is an example how I would showcase Data Analytics Portfolio: 1. Visualize: Dashboards: Craft intuitive dashboards with real-time data visualization, enabling clients to monitor performance effortlessly. 2. Synthesize: Anomaly Summary: Highlight case studies where AI-powered anomaly detection saved clients millions by identifying critical issues before they escalated. 3. Measure & Share: KPI dashboard: Showcase quantifiable ROI metrics from satisfied customers, emphasizing impact on their bottom line.