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Recruiters spend about 7 seconds on a CV. Your GitHub portfolio gets even less.
If someone lands on your profile and sees repos called project1, final-version, SQL-stuff, or untitled-2 — they are gone. Not because your work is bad. Because they cannot tell what it is fast enough to care.
This week's YouTube video walks through how to build a GitHub portfolio from scratch. But today I want to go deep on the two things most people get wrong: project naming and the README.
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Project naming — the first thing recruoter sees
If you use a generic project name, like SQL Project, Power BI Report, Python Analysis - it tells a recruiter nothing about what you actually did.
Think about how projects get named at a real job. Not "Excel Project." Something like "Monthly Sales Analysis — Fashion Retail, 2024–2026." Or "Marketing Campaign Efficiency Assessment, Q3 2025."
That is exactly how you should name your repos.
A good project name answers three questions at a glance: what did you analyse, what type of business was it, and roughly when. Use date ranges where they apply, is the best practice, as again at work you usually prepare analysis for a specific timeframe (a month, a quarter, YoY, MoM, etc.)
One more thing: when you are choosing a project, I would recommend to make it broad enough to be relevant to multiple industries.
A project about sales, customers, or marketing performance could apply almost anywhere. A project about dragon games belongs in your learning folder, not your portfolio. It is still good to practice on, but industry relevant project increases your chances to get a call back from a recruiter.
The README — more important than the code
This is the part many people either skip entirely or treat as an afterthought. It should be the opposite. The README is your presentation. It is the first thing a recruiter or hiring manager reads, and for many of them it is the only thing.
Think of it as explaining your work to top management. They do not want to read your code. They want to understand what problem you solved, what you found, and what they should do about it.
A README that gets read has five parts:
The business problem. Why did you do this project? What question were you trying to answer? One or two sentences. Not "I analysed sales data." Something like "The marketing team wanted to understand which customer segments were generating the most revenue and whether campaign spend was being allocated effectively."
The data. What dataset did you use, where did it come from, what time period does it cover, how many rows. Two or three sentences. This is the equivalent of your data overview slide.
The analysis and findings. What did you actually find? This is where you add screenshots of your charts, snippets of your key SQL queries, or images of your dashboard. Do not just describe what you did — show it. One or two visuals here make a huge difference.
Recommendations. What should the business do based on what you found? Even one sentence here signals that you think like an analyst, not just a coder.
Limitations and next steps. What data was missing? What would you do differently with more time or more data? This shows you understand that real analysis always has constraints.
This is an example of a README file, we did this project in one of the YouTube videos https://github.com/KarinaSamsonova/mall-customer-segmentation
What to do with Excel, SQL and Power BI projects
The same README structure applies regardless of your tool. But here is what changes:
For Excel — upload the file itself, then screenshot your pivot tables, charts, and key outputs into the README. A lone .xlsx file sitting in a repo with nothing else tells a recruiter nothing.
For SQL — upload your .sql files, and if you worked with multiple tables, add a schema diagram showing the relationships. It takes five minutes and shows you understand how data is actually structured.
For Power BI or Tableau — screenshot your dashboard and upload it. Describe what it shows and what decisions it supports: "Sales KPI dashboard with drill-down by region, month and product category."
This is a great example of PowerBI project https://github.com/stefanovertua/Pizza-Pioneers-Sales-Operations-Analysis-Power-BI-Dashboard/tree/main
The rule in all cases: a folder of files does not get you hired. The write-up does.
Project ideas to get you started
If you are not sure what to build, here are a few ideas that work well across industries and look good in a portfolio:
Sales performance analysis — which products, regions or time periods drive the most revenue
Customer segmentation — grouping customers by behaviour or value using RFM or clustering
Marketing campaign analysis — did the campaign actually work, and for which audience
Churn prediction — which customers are at risk of leaving and what is the revenue impact
HR attrition analysis — what factors predict employee turnover
Financial dashboard — revenue, costs and margins visualised over time
Fraud detection — identifying unusual transactions in financial or e-commerce data
Demand forecasting — predicting sales volume to support inventory or staffing decisions
Where to find data
Everyone knows Kaggle. Here are ten sources worth bookmarking — including some less known ones:
Kaggle — kaggle.com/datasets — thousands of datasets across every industry
UCI Machine Learning Repository — archive.ics.uci.edu — classic ML datasets, well documented
Google Dataset Search — datasetsearch.research.google.com — finds datasets across the entire web
FiveThirtyEight — github.com/fivethirtyeight/data — clean, documented datasets behind their journalism articles, great for sports, politics and culture projects
DrivenData — drivendata.org — data science competitions for social good, nonprofits and public health
AWS Open Data Registry — registry.opendata.aws — 600+ datasets including satellite imagery, climate and genomics
Harvard Dataverse — dataverse.harvard.edu — 130,000+ research datasets, citation-quality data
World Bank Open Data — data.worldbank.org — global development data covering finance, health and education
Yahoo Finance / yfinance — finance.yahoo.com — complete stock market history, any ticker, free. Python users can install the yfinance library for easy access
One thing you can do today
Go to your GitHub profile right now. Look at your repo names. If any of them do not immediately tell a stranger what the project is about — rename them. It takes 3 minutes and it makes your profile look significantly more professional instantly.
This week's YouTube video shows the full process — how to set up your first repo, what a good README looks like in practice, and a real example of a bad portfolio vs a good one side by side.
Watch it here → link
Keep pushing 💪,
Karina
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