Making a Tech Portfolio That Reflects Current Trends

For folks who use GitHub and are in early stage careers and hope to add GitHub as a value add to your profile - here is a note. If interviewing for an SDE role, GitHub projects that don't solve a problem and are just a coding exercise are not very helpful. This may sound perplexing but it needs to be said. : Hiring managers and tech leads (like me) look for problem-solvers. A repository full of practice exercises might show you can write code, but it doesn’t demonstrate that you can build impactful solutions. ► How to Make Your Projects Stand Out 1. Frame Them as Solutions: Instead of presenting your project as "just another app," position it as a business solution or a tool that solves a real-world problem. For example: - Instead of “Expense Tracker App,” say, “A tool for freelancers to manage and categorize expenses for tax season.” - Instead of “Weather App,” frame it as, “A weather app optimized for agricultural planning with location-based crop suggestions.” 2. Highlight the Problem It Solves: Every great project starts with a problem. Make it clear what problem you identified and how your project addresses it. - Example: “This tool was designed for small business owners who struggle with automating their daily sales tracking.” 3. Show Quantifiable Value: Numbers tell a story. Include metrics like: - How much time/money the solution saves. - How many users it could potentially impact. - Any test data or feedback you’ve collected. - Example: “This app reduced invoice processing time by 35% in a real-world test case.” 4. Document It Well: A project is only as good as its README. Include: - A brief description of the problem it solves. - Key features. - Instructions on how to run/test it. - Screenshots, GIFs, or a demo link to bring it to life. Having a GitHub full of clone apps or unfinished side projects sends the wrong signal. It doesn’t show creativity, ownership, or impact, it shows you can follow tutorials, and that’s not what companies hire for. Instead, invest your time into one or two high-impact projects that: - Solve real-world problems. - Show off your ability to understand user needs. - Demonstrate your thought process, design skills, and technical execution. So, take a step back, revisit your GitHub, and think: - Does this project solve a problem? - Can I explain its value to someone outside of tech? - Would I hire someone based on this work? If the answer isn’t “yes,” it’s time to rethink how you approach your projects. Remember: One impactful project > 100 clones. Focus on impact, not just output.

If you want a standout portfolio in 2025 as a beginner Data Scientist or AI Engineer, use this framework👇 1. Select a Meaningful Problem → Choose a real-world issue you're genuinely interested in (e.g., climate change prediction, healthcare improvements, social media analytics) → Clearly define the objective and the potential impact of solving this issue 2. Acquire and Document Data → Use reliable sources (Kaggle, UCI Repository, Hugging Face) → Clearly document your process for selecting and gathering the data 3. Data Preparation → Clean and preprocess the data thoroughly → Outline key steps (handling missing data, normalization, feature engineering) 4. Exploratory Data Analysis (EDA) → Generate visualizations and summary statistics → Clearly state insights and how they guide your modeling decisions 5. Select Appropriate Algorithms → Choose suitable methods (e.g., Transformer models, XGBoost, clustering) → Provide reasoning for your choice based on the problem and data 6. Develop and Optimize Your Model → Write clean, reproducible, and modular code → Clearly document model experimentation, model training, hyperparameter tuning, and validation steps 7. Evaluate Your Model → Use relevant metrics (ROC-AUC, F1-score, RMSE, BLEU, MMLU) → Present your evaluations clearly, including visualizations like ROC curves or confusion matrices 8. Analyze Results Critically Clearly interpret outcomes, discuss strengths, limitations, and biases Suggest realistic improvements and next steps 9. Deploy Your Model (Optional) → Create a simple web app using tools like Streamlit, Hugging Face Spaces, Flask, or FastAPI → Provide a working demo and clearly document its functionality 10. Comprehensive Documentation → Write a professional, detailed README. → Clearly summarize your project's purpose, methodology, results, and real-world relevance 11. Let your work talk → Share the code, data catalog, and documentation to reproduce on GitHub → Write a detailed blog about interesting insights and outcomes from the project, and share it on Substack/ Medium/ LinkedIn article You can use this framework to build as many projects as you like. While doing multiple projects make sure to explore different use-cases and different algorithms, which will help you get a holistic view of the Data & ML space. PS: LinkedIn post has character limit, so I will be sharing a list of portfolio projects I would recommend to start with, in the next post -------- Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources to keep you up-to-date about the AI space!

I have reviewed 100+ portfolio projects. If you want employers to hire you even without experience, Make sure your project does these 𝟲 things. A great portfolio isn’t just a collection of skills It’s a showcase of how you solve real problems. This is what makes a portfolio project stand out: => 𝗜𝘁 𝘁𝗲𝗹𝗹𝘀 𝗮 𝘀𝘁𝗼𝗿𝘆 Every strong project follows a simple arc: Problem → Solution → Impact. Make it clear what challenge you tackled, how you solved it, and the results. => 𝗜𝘁 𝘀𝗼𝗹𝘃𝗲𝘀 𝗮 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 The best projects come from real-world problems. Current events: Can you analyze a trending issue? (e.g., election results, COVID trends, mask effectiveness) Daily annoyances: What problem do you wish someone would solve? Do it yourself. => 𝗜𝘁 𝘀𝗵𝗼𝘄𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 Good projects highlight your decision-making and problem-solving. Where did you pivot? What obstacles did you overcome? Show your process. => 𝗣𝗮𝘀𝘀𝗶𝗼𝗻 𝗺𝗲𝗲𝘁𝘀 𝗽𝗿𝗼𝗳𝗶𝘁 The best projects happen where interest meets impact. Find a topic you enjoy, just make sure it’s valuable to potential employers. => 𝗜𝘁 𝘀𝗽𝗲𝗮𝗸𝘀 𝗳𝗼𝗿 𝗶𝘁𝘀𝗲𝗹𝗳 A great project saves you time in interviews. If it’s well-structured, you’ll only need to explain the context once. The results will do the rest. => 𝗜𝘁’𝘀 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 (𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀/𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀) Go beyond basic analysis and build interactive dashboards (Tableau, Power BI, Streamlit). Let your audience explore the data. A good portfolio project isn’t just technical It proves you can solve meaningful problems. Follow me, Jaret André to land the job you want 10x faster.

Stop Building Another Titanic Project! 🤦♂️ I have reviewed 133+ Data Science portfolios, and what I found was shocking: 90% had the same projects 80% never deployed them 50% couldn't explain their own code Let me be brutally honest - your Titanic survival prediction isn't going to land you that dream job. Here's what ACTUALLY impresses recruiters: 1. Real-World Impact Build products that solve actual problems. Forget academic datasets - create something people can use. 2. End-to-End Solutions Don't just stop at model building. Deploy your projects, create APIs, build user interfaces. Show you can deliver a complete solution. 3. Original Ideas House price prediction? Been there, done that. Think unique - maybe a tool that helps local businesses, or an app that solves a community problem. 4. Documentation Skills Clean code with clear documentation shows you can work in a team. If you can't explain your code, how will you collaborate with others? 5. Problem-Solving Approach Showcase your thinking process. The "why" behind your decisions matters more than the code itself. Want to stand out? I'm starting a cohort where we'll build real products together. No more cliché projects - let's create something meaningful. Comment your email 👇 and I'll notify you once the next cohort is open!