How do you present data science projects in a portfolio?

To be honest, based on my past 9 years of data science experience, I always seems organizations studying or trying to solve 2 key problems. They are: 1. Why customers leave and how can we retain them - Customer Churn 2. Where is the Revenue? - Sales Forecasting If we choose those projects that the organizations are trying to solve in real time then you can get the eye of the recruiter.

Selecting diverse and relevant projects is key in building a robust data science portfolio. Focus on projects aligned with your industry or desired role, showcasing a variety of data sources, methods, and tools used. For instance, projects analyzing customer behavior or sales trends demonstrate valuable skills for an e-commerce analyst role.

I disagree with the advice "Avoid jargons". Since you are showcasing your expertise in a particular domain through a portfolio project, you should use the domain specific jargons to demonstrate your understanding of the domain. Jargons help understand the problems and dive deeper into the problems. However, do introduce or define the terms when you use them, so that your audience can understand as well.

Clear and concise documentation is crucial when showcasing your data science projects. Provide enough context for your audience to understand the problem, data, approach, and results, while avoiding overwhelming them with jargon. Utilize visuals, code snippets, and metrics to enhance your narrative and demonstrate technical proficiency. Remember to review and edit for clarity, accuracy, and grammar.

Prioritizing accessibility, they underscore the significance of hosting data science projects online. Utilizing platforms like GitHub, Medium, or a personal website, they ensure a professional presentation of their work. Their emphasis lies in making projects navigable and compatible across devices and browsers, providing clear links to code, data, and outputs for transparency and exploration. Through rigorous testing for functionality and performance, they enhance the user experience.

Maintaining a data science portfolio is an ongoing process. Regular updates with new or enhanced projects are essential to reflect current skills and interests. Seeking feedback from colleagues, mentors, or potential employers/clients is crucial for portfolio improvement. Specific and constructive feedback on content, presentation, and impact helps refine the portfolio. Remaining open to learning from others' portfolios and incorporating best practices fosters continuous growth.

One thing that I have observed so far in terms of getting reach for your content is to tag other people and their work. If your project extends someone else's work, you can ask and tag them to the post, you're reach immediately expands.

Promoting your data science portfolio and networking are essential steps in showcasing your work effectively. Sharing your portfolio on platforms like LinkedIn, Twitter, or Reddit helps expand your reach within your network and beyond. Engaging in online communities and forums allows you to showcase your portfolio, learn from others, and discover new opportunities. Additionally, attending events and meetups provides valuable face-to-face networking opportunities with fellow data scientists. Being prepared to articulate your projects clearly and engagingly is key to making meaningful connections.

Beyond the structured advice provided, it's crucial to emphasize the value of continuous learning and adaptability in the field of data science. Staying updated with emerging technologies, methodologies, and industry trends is essential for professional growth. Additionally, fostering a curious mindset and exploring diverse datasets and problem domains can lead to innovative solutions and unique portfolio additions. Remember, the journey of building and showcasing a data science portfolio is not just about demonstrating expertise but also about demonstrating a passion for solving complex problems and making a meaningful impact.